2025-03-04T20:00:47.9575357Z Current runner version: '2.322.0'
2025-03-04T20:00:47.9582843Z Runner name: 'i-0f1aad72ac3d41ea9'
2025-03-04T20:00:47.9583697Z Runner group name: 'Default'
2025-03-04T20:00:47.9584807Z Machine name: 'ip-10-1-71-161'
2025-03-04T20:00:47.9589417Z ##[group]GITHUB_TOKEN Permissions
2025-03-04T20:00:47.9592349Z Actions: read
2025-03-04T20:00:47.9593090Z Attestations: read
2025-03-04T20:00:47.9593751Z Checks: read
2025-03-04T20:00:47.9594354Z Contents: read
2025-03-04T20:00:47.9595068Z Deployments: read
2025-03-04T20:00:47.9595725Z Discussions: read
2025-03-04T20:00:47.9596371Z Issues: read
2025-03-04T20:00:47.9597012Z Metadata: read
2025-03-04T20:00:47.9597650Z Packages: read
2025-03-04T20:00:47.9598306Z Pages: read
2025-03-04T20:00:47.9598953Z PullRequests: read
2025-03-04T20:00:47.9599601Z RepositoryProjects: read
2025-03-04T20:00:47.9600364Z SecurityEvents: read
2025-03-04T20:00:47.9601038Z Statuses: read
2025-03-04T20:00:47.9601675Z ##[endgroup]
2025-03-04T20:00:47.9605140Z Secret source: Actions
2025-03-04T20:00:47.9606209Z Prepare workflow directory
2025-03-04T20:00:48.0122430Z Prepare all required actions
2025-03-04T20:00:48.0162579Z Getting action download info
2025-03-04T20:00:48.1909008Z Download action repository 'pytorch/test-infra@main' (SHA:79438512a0632583899938d3b0277da78f5569e0)
2025-03-04T20:00:49.5852406Z Download action repository 'pytorch/pytorch@main' (SHA:439395c0ae0234b529fd1b5ce30efca68be93f97)
2025-03-04T20:01:02.2745611Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0)
2025-03-04T20:01:02.5548290Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2025-03-04T20:01:03.1300049Z Getting action download info
2025-03-04T20:01:03.2580202Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683)
2025-03-04T20:01:03.4567775Z Getting action download info
2025-03-04T20:01:03.5671965Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2025-03-04T20:01:03.7862846Z Getting action download info
2025-03-04T20:01:03.8959679Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2025-03-04T20:01:04.0532584Z Getting action download info
2025-03-04T20:01:04.2200884Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/148205/merge (d0654836237b89031de4353648b2c86ba3fc52f9)
2025-03-04T20:01:04.2203173Z ##[group] Inputs
2025-03-04T20:01:04.2203567Z   build-environment: linux-focal-py3.13-clang10
2025-03-04T20:01:04.2206525Z   test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}
2025-03-04T20:01:04.2209836Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:01:04.2210643Z   sync-tag: 
2025-03-04T20:01:04.2211499Z   timeout-minutes: 600
2025-03-04T20:01:04.2211788Z   use-gha: 
2025-03-04T20:01:04.2212029Z   dashboard-tag: 
2025-03-04T20:01:04.2212305Z   s3-bucket: gha-artifacts
2025-03-04T20:01:04.2212610Z   aws-role-to-assume: 
2025-03-04T20:01:04.2213214Z   disable-monitor: false
2025-03-04T20:01:04.2213803Z ##[endgroup]
2025-03-04T20:01:04.2214387Z Complete job name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:01:04.2683771Z A job started hook has been configured by the self-hosted runner administrator
2025-03-04T20:01:04.2796313Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2025-03-04T20:01:04.2805712Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:01:04.2806616Z ##[endgroup]
2025-03-04T20:01:05.4258029Z Runner Type: lf.linux.2xlarge
2025-03-04T20:01:05.4258602Z Instance Type: c5.2xlarge
2025-03-04T20:01:05.4258954Z AMI Name: unknown
2025-03-04T20:01:05.4286425Z AMI ID: ami-05b10e08d247fb927
2025-03-04T20:01:10.8090135Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2025-03-04T20:01:10.8090630Z with:
2025-03-04T20:01:10.8091334Z   github-secret: ***
2025-03-04T20:01:10.8092113Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2025-03-04T20:01:10.8092971Z   activate-with-label: false
2025-03-04T20:01:10.8093291Z   label: with-ssh
2025-03-04T20:01:10.8093583Z   remove-existing-keys: true
2025-03-04T20:01:10.8093898Z   fail-silently: true
2025-03-04T20:01:10.8094172Z env:
2025-03-04T20:01:10.8094416Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:01:10.8094714Z ##[endgroup]
2025-03-04T20:01:10.9222519Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2025-03-04T20:01:11.3159780Z Grabbing public ssh keys from https://github.com/williamwen42.keys
2025-03-04T20:01:11.3961910Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2025-03-04T20:01:11.3976103Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2025-03-04T20:01:11.4012064Z Login using: ssh ec2-user@ec2-3-235-65-16.compute-1.amazonaws.com
2025-03-04T20:01:11.4012728Z All testing is done inside the container, to start an interactive session run:
2025-03-04T20:01:11.4013372Z   docker exec -it $(docker container ps --format '{{.ID}}') bash
2025-03-04T20:01:11.4137552Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2025-03-04T20:01:11.4138134Z with:
2025-03-04T20:01:11.4138383Z   no-sudo: true
2025-03-04T20:01:11.4138654Z   submodules: recursive
2025-03-04T20:01:11.4138946Z   fetch-depth: 0
2025-03-04T20:01:11.4139190Z env:
2025-03-04T20:01:11.4139431Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:01:11.4139728Z ##[endgroup]
2025-03-04T20:01:11.4226946Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T20:01:11.4227973Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T20:01:11.4237593Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:01:11.4238041Z env:
2025-03-04T20:01:11.4238337Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:01:11.4238654Z ##[endgroup]
2025-03-04T20:01:11.4330293Z ##[group]Run retry () {
2025-03-04T20:01:11.4330685Z retry () {
2025-03-04T20:01:11.4331062Z   $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
2025-03-04T20:01:11.4331488Z }
2025-03-04T20:01:11.4331754Z echo "${GITHUB_WORKSPACE}"
2025-03-04T20:01:11.4332112Z if [ -z "${NO_SUDO}" ]; then
2025-03-04T20:01:11.4332487Z   retry sudo rm -rf "${GITHUB_WORKSPACE}"
2025-03-04T20:01:11.4332842Z else
2025-03-04T20:01:11.4333129Z   retry rm -rf "${GITHUB_WORKSPACE}"
2025-03-04T20:01:11.4333478Z fi
2025-03-04T20:01:11.4333741Z mkdir "${GITHUB_WORKSPACE}"
2025-03-04T20:01:11.4334068Z 
2025-03-04T20:01:11.4334346Z # Use all available CPUs for fetching
2025-03-04T20:01:11.4334719Z cd "${GITHUB_WORKSPACE}"
2025-03-04T20:01:11.4335081Z git config --global fetch.parallel 0
2025-03-04T20:01:11.4335687Z git config --global submodule.fetchJobs 0
2025-03-04T20:01:11.4341354Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:01:11.4341776Z env:
2025-03-04T20:01:11.4342024Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:01:11.4342318Z   NO_SUDO: true
2025-03-04T20:01:11.4342603Z ##[endgroup]
2025-03-04T20:01:11.4365284Z /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T20:01:11.4485775Z ##[group]Run actions/checkout@v4
2025-03-04T20:01:11.4486101Z with:
2025-03-04T20:01:11.4486376Z   ref: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:01:11.4486736Z   fetch-depth: 0
2025-03-04T20:01:11.4487004Z   submodules: recursive
2025-03-04T20:01:11.4487362Z   show-progress: false
2025-03-04T20:01:11.4487681Z   repository: pytorch/pytorch
2025-03-04T20:01:11.4488121Z   token: ***
2025-03-04T20:01:11.4488374Z   ssh-strict: true
2025-03-04T20:01:11.4488641Z   ssh-user: git
2025-03-04T20:01:11.4488919Z   persist-credentials: true
2025-03-04T20:01:11.4489220Z   clean: true
2025-03-04T20:01:11.4489508Z   sparse-checkout-cone-mode: true
2025-03-04T20:01:11.4489844Z   fetch-tags: false
2025-03-04T20:01:11.4490103Z   lfs: false
2025-03-04T20:01:11.4490362Z   set-safe-directory: true
2025-03-04T20:01:11.4490650Z env:
2025-03-04T20:01:11.4490890Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:01:11.4491184Z ##[endgroup]
2025-03-04T20:01:11.5576059Z Syncing repository: pytorch/pytorch
2025-03-04T20:01:11.5577491Z ##[group]Getting Git version info
2025-03-04T20:01:11.5578107Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-04T20:01:11.5578838Z [command]/usr/bin/git version
2025-03-04T20:01:11.5579158Z git version 2.47.1
2025-03-04T20:01:11.5591944Z ##[endgroup]
2025-03-04T20:01:11.5601277Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/f52e37e8-36a0-4cc4-a576-4cdb8fefa425/.gitconfig'
2025-03-04T20:01:11.5626186Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/f52e37e8-36a0-4cc4-a576-4cdb8fefa425' before making global git config changes
2025-03-04T20:01:11.5627499Z Adding repository directory to the temporary git global config as a safe directory
2025-03-04T20:01:11.5630230Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T20:01:11.5660322Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-04T20:01:11.5662984Z ##[group]Initializing the repository
2025-03-04T20:01:11.5666850Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T20:01:11.5695323Z hint: Using 'master' as the name for the initial branch. This default branch name
2025-03-04T20:01:11.5696446Z hint: is subject to change. To configure the initial branch name to use in all
2025-03-04T20:01:11.5697505Z hint: of your new repositories, which will suppress this warning, call:
2025-03-04T20:01:11.5698349Z hint:
2025-03-04T20:01:11.5698905Z hint: 	git config --global init.defaultBranch <name>
2025-03-04T20:01:11.5699615Z hint:
2025-03-04T20:01:11.5700267Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2025-03-04T20:01:11.5701308Z hint: 'development'. The just-created branch can be renamed via this command:
2025-03-04T20:01:11.5702123Z hint:
2025-03-04T20:01:11.5702570Z hint: 	git branch -m <name>
2025-03-04T20:01:11.5703491Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/
2025-03-04T20:01:11.5708555Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2025-03-04T20:01:11.5731845Z ##[endgroup]
2025-03-04T20:01:11.5732667Z ##[group]Disabling automatic garbage collection
2025-03-04T20:01:11.5736336Z [command]/usr/bin/git config --local gc.auto 0
2025-03-04T20:01:11.5759288Z ##[endgroup]
2025-03-04T20:01:11.5760050Z ##[group]Setting up auth
2025-03-04T20:01:11.5766226Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-04T20:01:11.5791122Z [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' || :"
2025-03-04T20:01:11.6047835Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-04T20:01:11.6071977Z [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' || :"
2025-03-04T20:01:11.6316557Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-04T20:01:11.6356489Z ##[endgroup]
2025-03-04T20:01:11.6356978Z ##[group]Fetching the repository
2025-03-04T20:01:11.6363676Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2025-03-04T20:02:06.7310726Z From https://github.com/pytorch/pytorch
2025-03-04T20:02:06.7311530Z  * [new branch]              2.1-dynamic-doc             -> origin/2.1-dynamic-doc
2025-03-04T20:02:06.7312141Z  * [new branch]              2.6.0.dev20241004+          -> origin/2.6.0.dev20241004+
2025-03-04T20:02:06.7312855Z  * [new branch]              20250219_e8m0_intermediate  -> origin/20250219_e8m0_intermediate
2025-03-04T20:02:06.7313528Z  * [new branch]              20250219_test               -> origin/20250219_test
2025-03-04T20:02:06.7314397Z  * [new branch]              Adjust-Description-for-linux-binary-test-Workflow -> origin/Adjust-Description-for-linux-binary-test-Workflow
2025-03-04T20:02:06.7315309Z  * [new branch]              Chillee-patch-5             -> origin/Chillee-patch-5
2025-03-04T20:02:06.7315986Z  * [new branch]              Flamefire-patch-1           -> origin/Flamefire-patch-1
2025-03-04T20:02:06.7316637Z  * [new branch]              HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes
2025-03-04T20:02:06.7317479Z  * [new branch]              JackCaoG/add_new_lazy_counter_macro -> origin/JackCaoG/add_new_lazy_counter_macro
2025-03-04T20:02:06.7319429Z  * [new branch]              JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops
2025-03-04T20:02:06.7320864Z  * [new branch]              JackCaoG/fix_xla_torchbench -> origin/JackCaoG/fix_xla_torchbench
2025-03-04T20:02:06.7321807Z  * [new branch]              JackCaoG/update_dynamo_doc  -> origin/JackCaoG/update_dynamo_doc
2025-03-04T20:02:06.7322744Z  * [new branch]              JackCaoG/update_xla_pin_to_skip_test -> origin/JackCaoG/update_xla_pin_to_skip_test
2025-03-04T20:02:06.7323994Z  * [new branch]              JackCaoG/update_xla_pin_to_skip_test2 -> origin/JackCaoG/update_xla_pin_to_skip_test2
2025-03-04T20:02:06.7325071Z  * [new branch]              NicolasHug-patch-2          -> origin/NicolasHug-patch-2
2025-03-04T20:02:06.7325979Z  * [new branch]              PR-AOTInductorNoneBug       -> origin/PR-AOTInductorNoneBug
2025-03-04T20:02:06.7327133Z  * [new branch]              PR-AOTInductorNoneBugFix    -> origin/PR-AOTInductorNoneBugFix
2025-03-04T20:02:06.7328224Z  * [new branch]              PR-FixConfigsIssue          -> origin/PR-FixConfigsIssue
2025-03-04T20:02:06.7329225Z  * [new branch]              PR-NoneBugFix-viable        -> origin/PR-NoneBugFix-viable
2025-03-04T20:02:06.7330246Z  * [new branch]              PR-ResetToZero              -> origin/PR-ResetToZero
2025-03-04T20:02:06.7331292Z  * [new branch]              Remove-linux_t4g_2xlarge-Usage -> origin/Remove-linux_t4g_2xlarge-Usage
2025-03-04T20:02:06.7332350Z  * [new branch]              Revert-PR-110949            -> origin/Revert-PR-110949
2025-03-04T20:02:06.7333311Z  * [new branch]              Update-Flash-Packaging      -> origin/Update-Flash-Packaging
2025-03-04T20:02:06.7334346Z  * [new branch]              Valentine/flash_attention_bf16 -> origin/Valentine/flash_attention_bf16
2025-03-04T20:02:06.7336065Z  * [new branch]              _tmp-orig/release/2.6       -> origin/_tmp-orig/release/2.6
2025-03-04T20:02:06.7337157Z  * [new branch]              _tmp-release/2.6            -> origin/_tmp-release/2.6
2025-03-04T20:02:06.7339024Z  * [new branch]              abock/onnx-1.15.0-validation -> origin/abock/onnx-1.15.0-validation
2025-03-04T20:02:06.7341775Z  * [new branch]              abock/ort-nightly==1.16.0.dev20230908001 -> origin/abock/ort-nightly==1.16.0.dev20230908001
2025-03-04T20:02:06.7389520Z  * [new branch]              add-android-build-workflow  -> origin/add-android-build-workflow
2025-03-04T20:02:06.7390771Z  * [new branch]              add-assign                  -> origin/add-assign
2025-03-04T20:02:06.7391942Z  * [new branch]              add_broadcast_functional_collective -> origin/add_broadcast_functional_collective
2025-03-04T20:02:06.7393072Z  * [new branch]              add_from_group_doc_and_test -> origin/add_from_group_doc_and_test
2025-03-04T20:02:06.7393764Z  * [new branch]              add_mha_to_autocast_policy  -> origin/add_mha_to_autocast_policy
2025-03-04T20:02:06.7394881Z  * [new branch]              add_non_parallel_model_comparison -> origin/add_non_parallel_model_comparison
2025-03-04T20:02:06.7395891Z  * [new branch]              add_test_to_show_view_gap   -> origin/add_test_to_show_view_gap
2025-03-04T20:02:06.7396597Z  * [new branch]              add_windows_testing_back    -> origin/add_windows_testing_back
2025-03-04T20:02:06.7397575Z  * [new branch]              addmm-heuristic             -> origin/addmm-heuristic
2025-03-04T20:02:06.7398116Z  * [new branch]              addsimde                    -> origin/addsimde
2025-03-04T20:02:06.7398866Z  * [new branch]              adi/gemm_bf16f32            -> origin/adi/gemm_bf16f32
2025-03-04T20:02:06.7399624Z  * [new branch]              ah-globalfeedback-hook      -> origin/ah-globalfeedback-hook
2025-03-04T20:02:06.7400386Z  * [new branch]              alanwaketan/pin2            -> origin/alanwaketan/pin2
2025-03-04T20:02:06.7401220Z  * [new branch]              albanD-patch-1              -> origin/albanD-patch-1
2025-03-04T20:02:06.7402129Z  * [new branch]              albanD-patch-2              -> origin/albanD-patch-2
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2025-03-04T20:02:07.2140942Z  * [new branch]              mlazos/foreach-op           -> origin/mlazos/foreach-op
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2025-03-04T20:02:07.2180829Z  * [new branch]              mlazos/proxy-ctors          -> origin/mlazos/proxy-ctors
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2025-03-04T20:02:07.2182812Z  * [new branch]              mlazos/pt                   -> origin/mlazos/pt
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2025-03-04T20:02:07.2187216Z  * [new branch]              mlazos/sdpa-driss           -> origin/mlazos/sdpa-driss
2025-03-04T20:02:07.2188277Z  * [new branch]              mlazos/static-inputs-log    -> origin/mlazos/static-inputs-log
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2025-03-04T20:02:07.2190042Z  * [new branch]              mlazos/td-fix2              -> origin/mlazos/td-fix2
2025-03-04T20:02:07.2191049Z  * [new branch]              mlazos/tensor-hasattr2      -> origin/mlazos/tensor-hasattr2
2025-03-04T20:02:07.2191874Z  * [new branch]              mlazos/tensor-inherit-backup -> origin/mlazos/tensor-inherit-backup
2025-03-04T20:02:07.2192904Z  * [new branch]              mlazos/tensor-like-fix      -> origin/mlazos/tensor-like-fix
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2025-03-04T20:02:07.2195212Z  * [new branch]              mlazos/tensor-lr2           -> origin/mlazos/tensor-lr2
2025-03-04T20:02:07.2195958Z  * [new branch]              mlazos/tf-inherit           -> origin/mlazos/tf-inherit
2025-03-04T20:02:07.2197050Z  * [new branch]              mlazos/tf-mode              -> origin/mlazos/tf-mode
2025-03-04T20:02:07.2198040Z  * [new branch]              mlazos/tf-mode-backup2      -> origin/mlazos/tf-mode-backup2
2025-03-04T20:02:07.2198998Z  * [new branch]              mlazos/tf-mode-reland       -> origin/mlazos/tf-mode-reland
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2025-03-04T20:02:07.2201177Z  * [new branch]              mlazos/tf-mode-reland3      -> origin/mlazos/tf-mode-reland3
2025-03-04T20:02:07.2202101Z  * [new branch]              mlazos/tf-refactor          -> origin/mlazos/tf-refactor
2025-03-04T20:02:07.2203188Z  * [new branch]              mlazos/tf-subclass-stack    -> origin/mlazos/tf-subclass-stack
2025-03-04T20:02:07.2204142Z  * [new branch]              mlazos/tf-trace-full        -> origin/mlazos/tf-trace-full
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2025-03-04T20:02:07.2206035Z  * [new branch]              mlazos/tune-proto           -> origin/mlazos/tune-proto
2025-03-04T20:02:07.2207014Z  * [new branch]              mlazos/vary-beta            -> origin/mlazos/vary-beta
2025-03-04T20:02:07.2207995Z  * [new branch]              mlazos/vary-beta2           -> origin/mlazos/vary-beta2
2025-03-04T20:02:07.2208970Z  * [new branch]              mlazos/weird-perf1          -> origin/mlazos/weird-perf1
2025-03-04T20:02:07.2210129Z  * [new branch]              mod_guards1                 -> origin/mod_guards1
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2025-03-04T20:02:07.2214967Z  * [new branch]              mradmila/host_stats         -> origin/mradmila/host_stats
2025-03-04T20:02:07.2216106Z  * [new branch]              msaroufim-patch-10          -> origin/msaroufim-patch-10
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2025-03-04T20:02:07.2227058Z  * [new branch]              nestedfairseq2ops1          -> origin/nestedfairseq2ops1
2025-03-04T20:02:07.2228092Z  * [new branch]              new-batch-norm              -> origin/new-batch-norm
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2025-03-04T20:02:07.2235907Z  * [new branch]              nightly                     -> origin/nightly
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2025-03-04T20:02:07.2678642Z  * [new tag]                 ciflow/inductor-cu126/140793 -> ciflow/inductor-cu126/140793
2025-03-04T20:02:07.2679931Z  * [new tag]                 ciflow/inductor-micro-benchmark/141910 -> ciflow/inductor-micro-benchmark/141910
2025-03-04T20:02:07.2680672Z  * [new tag]                 ciflow/inductor-perf-compare/140195 -> ciflow/inductor-perf-compare/140195
2025-03-04T20:02:07.2681462Z  * [new tag]                 ciflow/inductor-perf-test-nightly/140195 -> ciflow/inductor-perf-test-nightly/140195
2025-03-04T20:02:07.2682415Z  * [new tag]                 ciflow/inductor-periodic/140793 -> ciflow/inductor-periodic/140793
2025-03-04T20:02:07.2683284Z  * [new tag]                 ciflow/inductor-periodic/145612 -> ciflow/inductor-periodic/145612
2025-03-04T20:02:07.2683871Z  * [new tag]                 ciflow/inductor-periodic/147315 -> ciflow/inductor-periodic/147315
2025-03-04T20:02:07.2684860Z  * [new tag]                 ciflow/inductor-rocm/140989 -> ciflow/inductor-rocm/140989
2025-03-04T20:02:07.2685629Z  * [new tag]                 ciflow/inductor-rocm/141309 -> ciflow/inductor-rocm/141309
2025-03-04T20:02:07.2686390Z  * [new tag]                 ciflow/inductor-rocm/141355 -> ciflow/inductor-rocm/141355
2025-03-04T20:02:07.2687169Z  * [new tag]                 ciflow/inductor-rocm/146264 -> ciflow/inductor-rocm/146264
2025-03-04T20:02:07.2688131Z  * [new tag]                 ciflow/inductor-rocm/146903 -> ciflow/inductor-rocm/146903
2025-03-04T20:02:07.2688604Z  * [new tag]                 ciflow/inductor-rocm/147315 -> ciflow/inductor-rocm/147315
2025-03-04T20:02:07.2689339Z  * [new tag]                 ciflow/inductor-rocm/147320 -> ciflow/inductor-rocm/147320
2025-03-04T20:02:07.2690164Z  * [new tag]                 ciflow/inductor-rocm/147452 -> ciflow/inductor-rocm/147452
2025-03-04T20:02:07.2691131Z  * [new tag]                 ciflow/inductor-rocm/147583 -> ciflow/inductor-rocm/147583
2025-03-04T20:02:07.2691884Z  * [new tag]                 ciflow/inductor-rocm/147619 -> ciflow/inductor-rocm/147619
2025-03-04T20:02:07.2692847Z  * [new tag]                 ciflow/inductor-rocm/148305 -> ciflow/inductor-rocm/148305
2025-03-04T20:02:07.2693782Z  * [new tag]                 ciflow/inductor-rocm/148437 -> ciflow/inductor-rocm/148437
2025-03-04T20:02:07.2694532Z  * [new tag]                 ciflow/inductor/110155      -> ciflow/inductor/110155
2025-03-04T20:02:07.2695135Z  * [new tag]                 ciflow/inductor/113257      -> ciflow/inductor/113257
2025-03-04T20:02:07.2695754Z  * [new tag]                 ciflow/inductor/119496      -> ciflow/inductor/119496
2025-03-04T20:02:07.2696362Z  * [new tag]                 ciflow/inductor/119977      -> ciflow/inductor/119977
2025-03-04T20:02:07.2696982Z  * [new tag]                 ciflow/inductor/120076      -> ciflow/inductor/120076
2025-03-04T20:02:07.2697578Z  * [new tag]                 ciflow/inductor/121445      -> ciflow/inductor/121445
2025-03-04T20:02:07.2698264Z  * [new tag]                 ciflow/inductor/124490      -> ciflow/inductor/124490
2025-03-04T20:02:07.2698918Z  * [new tag]                 ciflow/inductor/125270      -> ciflow/inductor/125270
2025-03-04T20:02:07.2699533Z  * [new tag]                 ciflow/inductor/125326      -> ciflow/inductor/125326
2025-03-04T20:02:07.2700314Z  * [new tag]                 ciflow/inductor/125428      -> ciflow/inductor/125428
2025-03-04T20:02:07.2700838Z  * [new tag]                 ciflow/inductor/125469      -> ciflow/inductor/125469
2025-03-04T20:02:07.2701978Z  * [new tag]                 ciflow/inductor/125806      -> ciflow/inductor/125806
2025-03-04T20:02:07.2702943Z  * [new tag]                 ciflow/inductor/125888      -> ciflow/inductor/125888
2025-03-04T20:02:07.2704020Z  * [new tag]                 ciflow/inductor/125995      -> ciflow/inductor/125995
2025-03-04T20:02:07.2704743Z  * [new tag]                 ciflow/inductor/126348      -> ciflow/inductor/126348
2025-03-04T20:02:07.2705475Z  * [new tag]                 ciflow/inductor/127011      -> ciflow/inductor/127011
2025-03-04T20:02:07.2706439Z  * [new tag]                 ciflow/inductor/127171      -> ciflow/inductor/127171
2025-03-04T20:02:07.2707128Z  * [new tag]                 ciflow/inductor/127293      -> ciflow/inductor/127293
2025-03-04T20:02:07.2707897Z  * [new tag]                 ciflow/inductor/127294      -> ciflow/inductor/127294
2025-03-04T20:02:07.2708872Z  * [new tag]                 ciflow/inductor/129352      -> ciflow/inductor/129352
2025-03-04T20:02:07.2709576Z  * [new tag]                 ciflow/inductor/129420      -> ciflow/inductor/129420
2025-03-04T20:02:07.2710305Z  * [new tag]                 ciflow/inductor/130141      -> ciflow/inductor/130141
2025-03-04T20:02:07.2711353Z  * [new tag]                 ciflow/inductor/130499      -> ciflow/inductor/130499
2025-03-04T20:02:07.2712015Z  * [new tag]                 ciflow/inductor/130887      -> ciflow/inductor/130887
2025-03-04T20:02:07.2712781Z  * [new tag]                 ciflow/inductor/131354      -> ciflow/inductor/131354
2025-03-04T20:02:07.2713484Z  * [new tag]                 ciflow/inductor/132021      -> ciflow/inductor/132021
2025-03-04T20:02:07.2714246Z  * [new tag]                 ciflow/inductor/132414      -> ciflow/inductor/132414
2025-03-04T20:02:07.2714984Z  * [new tag]                 ciflow/inductor/133044      -> ciflow/inductor/133044
2025-03-04T20:02:07.2716309Z  * [new tag]                 ciflow/inductor/133121      -> ciflow/inductor/133121
2025-03-04T20:02:07.2717068Z  * [new tag]                 ciflow/inductor/133287      -> ciflow/inductor/133287
2025-03-04T20:02:07.2717769Z  * [new tag]                 ciflow/inductor/133289      -> ciflow/inductor/133289
2025-03-04T20:02:07.2718491Z  * [new tag]                 ciflow/inductor/133296      -> ciflow/inductor/133296
2025-03-04T20:02:07.2719223Z  * [new tag]                 ciflow/inductor/133297      -> ciflow/inductor/133297
2025-03-04T20:02:07.2719971Z  * [new tag]                 ciflow/inductor/133315      -> ciflow/inductor/133315
2025-03-04T20:02:07.2720696Z  * [new tag]                 ciflow/inductor/133392      -> ciflow/inductor/133392
2025-03-04T20:02:07.2721447Z  * [new tag]                 ciflow/inductor/133419      -> ciflow/inductor/133419
2025-03-04T20:02:07.2722193Z  * [new tag]                 ciflow/inductor/133423      -> ciflow/inductor/133423
2025-03-04T20:02:07.2722913Z  * [new tag]                 ciflow/inductor/133667      -> ciflow/inductor/133667
2025-03-04T20:02:07.2723649Z  * [new tag]                 ciflow/inductor/133753      -> ciflow/inductor/133753
2025-03-04T20:02:07.2724364Z  * [new tag]                 ciflow/inductor/134681      -> ciflow/inductor/134681
2025-03-04T20:02:07.2725300Z  * [new tag]                 ciflow/inductor/135708      -> ciflow/inductor/135708
2025-03-04T20:02:07.2725948Z  * [new tag]                 ciflow/inductor/135792      -> ciflow/inductor/135792
2025-03-04T20:02:07.2726702Z  * [new tag]                 ciflow/inductor/136355      -> ciflow/inductor/136355
2025-03-04T20:02:07.2727434Z  * [new tag]                 ciflow/inductor/136702      -> ciflow/inductor/136702
2025-03-04T20:02:07.2728322Z  * [new tag]                 ciflow/inductor/137400      -> ciflow/inductor/137400
2025-03-04T20:02:07.2729002Z  * [new tag]                 ciflow/inductor/137568      -> ciflow/inductor/137568
2025-03-04T20:02:07.2729720Z  * [new tag]                 ciflow/inductor/137583      -> ciflow/inductor/137583
2025-03-04T20:02:07.2730664Z  * [new tag]                 ciflow/inductor/137846      -> ciflow/inductor/137846
2025-03-04T20:02:07.2731369Z  * [new tag]                 ciflow/inductor/137884      -> ciflow/inductor/137884
2025-03-04T20:02:07.2732107Z  * [new tag]                 ciflow/inductor/138185      -> ciflow/inductor/138185
2025-03-04T20:02:07.2732837Z  * [new tag]                 ciflow/inductor/138202      -> ciflow/inductor/138202
2025-03-04T20:02:07.2733582Z  * [new tag]                 ciflow/inductor/138213      -> ciflow/inductor/138213
2025-03-04T20:02:07.2734313Z  * [new tag]                 ciflow/inductor/138214      -> ciflow/inductor/138214
2025-03-04T20:02:07.2735258Z  * [new tag]                 ciflow/inductor/138388      -> ciflow/inductor/138388
2025-03-04T20:02:07.2736117Z  * [new tag]                 ciflow/inductor/138513      -> ciflow/inductor/138513
2025-03-04T20:02:07.2736811Z  * [new tag]                 ciflow/inductor/138519      -> ciflow/inductor/138519
2025-03-04T20:02:07.2737864Z  * [new tag]                 ciflow/inductor/138555      -> ciflow/inductor/138555
2025-03-04T20:02:07.2738869Z  * [new tag]                 ciflow/inductor/138626      -> ciflow/inductor/138626
2025-03-04T20:02:07.2739586Z  * [new tag]                 ciflow/inductor/138889      -> ciflow/inductor/138889
2025-03-04T20:02:07.2740340Z  * [new tag]                 ciflow/inductor/138930      -> ciflow/inductor/138930
2025-03-04T20:02:07.2741247Z  * [new tag]                 ciflow/inductor/139094      -> ciflow/inductor/139094
2025-03-04T20:02:07.2744446Z  * [new tag]                 ciflow/inductor/139271      -> ciflow/inductor/139271
2025-03-04T20:02:07.2744717Z  * [new tag]                 ciflow/inductor/139561      -> ciflow/inductor/139561
2025-03-04T20:02:07.2744918Z  * [new tag]                 ciflow/inductor/139975      -> ciflow/inductor/139975
2025-03-04T20:02:07.2745131Z  * [new tag]                 ciflow/inductor/140032      -> ciflow/inductor/140032
2025-03-04T20:02:07.2745513Z  * [new tag]                 ciflow/inductor/140084      -> ciflow/inductor/140084
2025-03-04T20:02:07.2745730Z  * [new tag]                 ciflow/inductor/140159      -> ciflow/inductor/140159
2025-03-04T20:02:07.2746362Z  * [new tag]                 ciflow/inductor/140195      -> ciflow/inductor/140195
2025-03-04T20:02:07.2747277Z  * [new tag]                 ciflow/inductor/140746      -> ciflow/inductor/140746
2025-03-04T20:02:07.2747995Z  * [new tag]                 ciflow/inductor/140756      -> ciflow/inductor/140756
2025-03-04T20:02:07.2748933Z  * [new tag]                 ciflow/inductor/140979      -> ciflow/inductor/140979
2025-03-04T20:02:07.2750113Z  * [new tag]                 ciflow/inductor/141082      -> ciflow/inductor/141082
2025-03-04T20:02:07.2750840Z  * [new tag]                 ciflow/inductor/141096      -> ciflow/inductor/141096
2025-03-04T20:02:07.2751558Z  * [new tag]                 ciflow/inductor/141097      -> ciflow/inductor/141097
2025-03-04T20:02:07.2752670Z  * [new tag]                 ciflow/inductor/141213      -> ciflow/inductor/141213
2025-03-04T20:02:07.2753565Z  * [new tag]                 ciflow/inductor/141309      -> ciflow/inductor/141309
2025-03-04T20:02:07.2754368Z  * [new tag]                 ciflow/inductor/141393      -> ciflow/inductor/141393
2025-03-04T20:02:07.2755083Z  * [new tag]                 ciflow/inductor/141641      -> ciflow/inductor/141641
2025-03-04T20:02:07.2755820Z  * [new tag]                 ciflow/inductor/141684      -> ciflow/inductor/141684
2025-03-04T20:02:07.2756553Z  * [new tag]                 ciflow/inductor/141700      -> ciflow/inductor/141700
2025-03-04T20:02:07.2757511Z  * [new tag]                 ciflow/inductor/141730      -> ciflow/inductor/141730
2025-03-04T20:02:07.2758222Z  * [new tag]                 ciflow/inductor/141842      -> ciflow/inductor/141842
2025-03-04T20:02:07.2758952Z  * [new tag]                 ciflow/inductor/141889      -> ciflow/inductor/141889
2025-03-04T20:02:07.2759735Z  * [new tag]                 ciflow/inductor/141940      -> ciflow/inductor/141940
2025-03-04T20:02:07.2760457Z  * [new tag]                 ciflow/inductor/141944      -> ciflow/inductor/141944
2025-03-04T20:02:07.2761230Z  * [new tag]                 ciflow/inductor/141961      -> ciflow/inductor/141961
2025-03-04T20:02:07.2762142Z  * [new tag]                 ciflow/inductor/142091      -> ciflow/inductor/142091
2025-03-04T20:02:07.2762869Z  * [new tag]                 ciflow/inductor/142092      -> ciflow/inductor/142092
2025-03-04T20:02:07.2763829Z  * [new tag]                 ciflow/inductor/142163      -> ciflow/inductor/142163
2025-03-04T20:02:07.2764903Z  * [new tag]                 ciflow/inductor/142272      -> ciflow/inductor/142272
2025-03-04T20:02:07.2765970Z  * [new tag]                 ciflow/inductor/142273      -> ciflow/inductor/142273
2025-03-04T20:02:07.2766680Z  * [new tag]                 ciflow/inductor/142295      -> ciflow/inductor/142295
2025-03-04T20:02:07.2767912Z  * [new tag]                 ciflow/inductor/142296      -> ciflow/inductor/142296
2025-03-04T20:02:07.2768807Z  * [new tag]                 ciflow/inductor/142309      -> ciflow/inductor/142309
2025-03-04T20:02:07.2769534Z  * [new tag]                 ciflow/inductor/142350      -> ciflow/inductor/142350
2025-03-04T20:02:07.2770288Z  * [new tag]                 ciflow/inductor/142372      -> ciflow/inductor/142372
2025-03-04T20:02:07.2771155Z  * [new tag]                 ciflow/inductor/142483      -> ciflow/inductor/142483
2025-03-04T20:02:07.2771859Z  * [new tag]                 ciflow/inductor/142851      -> ciflow/inductor/142851
2025-03-04T20:02:07.2772749Z  * [new tag]                 ciflow/inductor/143044      -> ciflow/inductor/143044
2025-03-04T20:02:07.2773445Z  * [new tag]                 ciflow/inductor/143103      -> ciflow/inductor/143103
2025-03-04T20:02:07.2774504Z  * [new tag]                 ciflow/inductor/143220      -> ciflow/inductor/143220
2025-03-04T20:02:07.2775363Z  * [new tag]                 ciflow/inductor/143256      -> ciflow/inductor/143256
2025-03-04T20:02:07.2776256Z  * [new tag]                 ciflow/inductor/143275      -> ciflow/inductor/143275
2025-03-04T20:02:07.2776858Z  * [new tag]                 ciflow/inductor/143313      -> ciflow/inductor/143313
2025-03-04T20:02:07.2777590Z  * [new tag]                 ciflow/inductor/143411      -> ciflow/inductor/143411
2025-03-04T20:02:07.2778547Z  * [new tag]                 ciflow/inductor/143457      -> ciflow/inductor/143457
2025-03-04T20:02:07.2779676Z  * [new tag]                 ciflow/inductor/143464      -> ciflow/inductor/143464
2025-03-04T20:02:07.2780456Z  * [new tag]                 ciflow/inductor/143475      -> ciflow/inductor/143475
2025-03-04T20:02:07.2781198Z  * [new tag]                 ciflow/inductor/143525      -> ciflow/inductor/143525
2025-03-04T20:02:07.2782294Z  * [new tag]                 ciflow/inductor/143527      -> ciflow/inductor/143527
2025-03-04T20:02:07.2783056Z  * [new tag]                 ciflow/inductor/143533      -> ciflow/inductor/143533
2025-03-04T20:02:07.2783787Z  * [new tag]                 ciflow/inductor/143534      -> ciflow/inductor/143534
2025-03-04T20:02:07.2784706Z  * [new tag]                 ciflow/inductor/143544      -> ciflow/inductor/143544
2025-03-04T20:02:07.2785590Z  * [new tag]                 ciflow/inductor/143666      -> ciflow/inductor/143666
2025-03-04T20:02:07.2786344Z  * [new tag]                 ciflow/inductor/143671      -> ciflow/inductor/143671
2025-03-04T20:02:07.2787054Z  * [new tag]                 ciflow/inductor/143712      -> ciflow/inductor/143712
2025-03-04T20:02:07.2787798Z  * [new tag]                 ciflow/inductor/143812      -> ciflow/inductor/143812
2025-03-04T20:02:07.2788721Z  * [new tag]                 ciflow/inductor/143833      -> ciflow/inductor/143833
2025-03-04T20:02:07.2789637Z  * [new tag]                 ciflow/inductor/143961      -> ciflow/inductor/143961
2025-03-04T20:02:07.2790396Z  * [new tag]                 ciflow/inductor/143987      -> ciflow/inductor/143987
2025-03-04T20:02:07.2791303Z  * [new tag]                 ciflow/inductor/144008      -> ciflow/inductor/144008
2025-03-04T20:02:07.2792095Z  * [new tag]                 ciflow/inductor/144017      -> ciflow/inductor/144017
2025-03-04T20:02:07.2792836Z  * [new tag]                 ciflow/inductor/144073      -> ciflow/inductor/144073
2025-03-04T20:02:07.2793494Z  * [new tag]                 ciflow/inductor/144097      -> ciflow/inductor/144097
2025-03-04T20:02:07.2794151Z  * [new tag]                 ciflow/inductor/144120      -> ciflow/inductor/144120
2025-03-04T20:02:07.2795109Z  * [new tag]                 ciflow/inductor/144172      -> ciflow/inductor/144172
2025-03-04T20:02:07.2795903Z  * [new tag]                 ciflow/inductor/144234      -> ciflow/inductor/144234
2025-03-04T20:02:07.2796629Z  * [new tag]                 ciflow/inductor/144272      -> ciflow/inductor/144272
2025-03-04T20:02:07.2797290Z  * [new tag]                 ciflow/inductor/144288      -> ciflow/inductor/144288
2025-03-04T20:02:07.2797967Z  * [new tag]                 ciflow/inductor/144293      -> ciflow/inductor/144293
2025-03-04T20:02:07.2799029Z  * [new tag]                 ciflow/inductor/144294      -> ciflow/inductor/144294
2025-03-04T20:02:07.2799785Z  * [new tag]                 ciflow/inductor/144332      -> ciflow/inductor/144332
2025-03-04T20:02:07.2800381Z  * [new tag]                 ciflow/inductor/144333      -> ciflow/inductor/144333
2025-03-04T20:02:07.2801068Z  * [new tag]                 ciflow/inductor/144349      -> ciflow/inductor/144349
2025-03-04T20:02:07.2801750Z  * [new tag]                 ciflow/inductor/144353      -> ciflow/inductor/144353
2025-03-04T20:02:07.2802537Z  * [new tag]                 ciflow/inductor/144365      -> ciflow/inductor/144365
2025-03-04T20:02:07.2803296Z  * [new tag]                 ciflow/inductor/144366      -> ciflow/inductor/144366
2025-03-04T20:02:07.2803951Z  * [new tag]                 ciflow/inductor/144405      -> ciflow/inductor/144405
2025-03-04T20:02:07.2804759Z  * [new tag]                 ciflow/inductor/144413      -> ciflow/inductor/144413
2025-03-04T20:02:07.2805310Z  * [new tag]                 ciflow/inductor/144414      -> ciflow/inductor/144414
2025-03-04T20:02:07.2805995Z  * [new tag]                 ciflow/inductor/144438      -> ciflow/inductor/144438
2025-03-04T20:02:07.2806642Z  * [new tag]                 ciflow/inductor/144452      -> ciflow/inductor/144452
2025-03-04T20:02:07.2807324Z  * [new tag]                 ciflow/inductor/144458      -> ciflow/inductor/144458
2025-03-04T20:02:07.2807948Z  * [new tag]                 ciflow/inductor/144501      -> ciflow/inductor/144501
2025-03-04T20:02:07.2808602Z  * [new tag]                 ciflow/inductor/144505      -> ciflow/inductor/144505
2025-03-04T20:02:07.2809275Z  * [new tag]                 ciflow/inductor/144507      -> ciflow/inductor/144507
2025-03-04T20:02:07.2809925Z  * [new tag]                 ciflow/inductor/144516      -> ciflow/inductor/144516
2025-03-04T20:02:07.2810601Z  * [new tag]                 ciflow/inductor/144542      -> ciflow/inductor/144542
2025-03-04T20:02:07.2811230Z  * [new tag]                 ciflow/inductor/144548      -> ciflow/inductor/144548
2025-03-04T20:02:07.2811897Z  * [new tag]                 ciflow/inductor/144551      -> ciflow/inductor/144551
2025-03-04T20:02:07.2812636Z  * [new tag]                 ciflow/inductor/144553      -> ciflow/inductor/144553
2025-03-04T20:02:07.2813301Z  * [new tag]                 ciflow/inductor/144555      -> ciflow/inductor/144555
2025-03-04T20:02:07.2813952Z  * [new tag]                 ciflow/inductor/144556      -> ciflow/inductor/144556
2025-03-04T20:02:07.2814617Z  * [new tag]                 ciflow/inductor/144579      -> ciflow/inductor/144579
2025-03-04T20:02:07.2815422Z  * [new tag]                 ciflow/inductor/144598      -> ciflow/inductor/144598
2025-03-04T20:02:07.2816090Z  * [new tag]                 ciflow/inductor/144712      -> ciflow/inductor/144712
2025-03-04T20:02:07.2817194Z  * [new tag]                 ciflow/inductor/144721      -> ciflow/inductor/144721
2025-03-04T20:02:07.2818135Z  * [new tag]                 ciflow/inductor/144724      -> ciflow/inductor/144724
2025-03-04T20:02:07.2818880Z  * [new tag]                 ciflow/inductor/144733      -> ciflow/inductor/144733
2025-03-04T20:02:07.2819569Z  * [new tag]                 ciflow/inductor/144741      -> ciflow/inductor/144741
2025-03-04T20:02:07.2820190Z  * [new tag]                 ciflow/inductor/144765      -> ciflow/inductor/144765
2025-03-04T20:02:07.2820929Z  * [new tag]                 ciflow/inductor/144771      -> ciflow/inductor/144771
2025-03-04T20:02:07.2821728Z  * [new tag]                 ciflow/inductor/144880      -> ciflow/inductor/144880
2025-03-04T20:02:07.2822398Z  * [new tag]                 ciflow/inductor/144905      -> ciflow/inductor/144905
2025-03-04T20:02:07.2823055Z  * [new tag]                 ciflow/inductor/144925      -> ciflow/inductor/144925
2025-03-04T20:02:07.2823759Z  * [new tag]                 ciflow/inductor/144943      -> ciflow/inductor/144943
2025-03-04T20:02:07.2824490Z  * [new tag]                 ciflow/inductor/144953      -> ciflow/inductor/144953
2025-03-04T20:02:07.2825169Z  * [new tag]                 ciflow/inductor/144975      -> ciflow/inductor/144975
2025-03-04T20:02:07.2825816Z  * [new tag]                 ciflow/inductor/144979      -> ciflow/inductor/144979
2025-03-04T20:02:07.2826550Z  * [new tag]                 ciflow/inductor/144986      -> ciflow/inductor/144986
2025-03-04T20:02:07.2827279Z  * [new tag]                 ciflow/inductor/144992      -> ciflow/inductor/144992
2025-03-04T20:02:07.2827966Z  * [new tag]                 ciflow/inductor/145024      -> ciflow/inductor/145024
2025-03-04T20:02:07.2828641Z  * [new tag]                 ciflow/inductor/145061      -> ciflow/inductor/145061
2025-03-04T20:02:07.2829337Z  * [new tag]                 ciflow/inductor/145117      -> ciflow/inductor/145117
2025-03-04T20:02:07.2830251Z  * [new tag]                 ciflow/inductor/145119      -> ciflow/inductor/145119
2025-03-04T20:02:07.2830763Z  * [new tag]                 ciflow/inductor/145150      -> ciflow/inductor/145150
2025-03-04T20:02:07.2831436Z  * [new tag]                 ciflow/inductor/145153      -> ciflow/inductor/145153
2025-03-04T20:02:07.2832131Z  * [new tag]                 ciflow/inductor/145254      -> ciflow/inductor/145254
2025-03-04T20:02:07.2832689Z  * [new tag]                 ciflow/inductor/145331      -> ciflow/inductor/145331
2025-03-04T20:02:07.2833369Z  * [new tag]                 ciflow/inductor/145353      -> ciflow/inductor/145353
2025-03-04T20:02:07.2834013Z  * [new tag]                 ciflow/inductor/145475      -> ciflow/inductor/145475
2025-03-04T20:02:07.2834726Z  * [new tag]                 ciflow/inductor/145523      -> ciflow/inductor/145523
2025-03-04T20:02:07.2835428Z  * [new tag]                 ciflow/inductor/145540      -> ciflow/inductor/145540
2025-03-04T20:02:07.2836110Z  * [new tag]                 ciflow/inductor/145559      -> ciflow/inductor/145559
2025-03-04T20:02:07.2836761Z  * [new tag]                 ciflow/inductor/145562      -> ciflow/inductor/145562
2025-03-04T20:02:07.2837449Z  * [new tag]                 ciflow/inductor/145594      -> ciflow/inductor/145594
2025-03-04T20:02:07.2838084Z  * [new tag]                 ciflow/inductor/145595      -> ciflow/inductor/145595
2025-03-04T20:02:07.2838731Z  * [new tag]                 ciflow/inductor/145605      -> ciflow/inductor/145605
2025-03-04T20:02:07.2839418Z  * [new tag]                 ciflow/inductor/145612      -> ciflow/inductor/145612
2025-03-04T20:02:07.2840092Z  * [new tag]                 ciflow/inductor/145636      -> ciflow/inductor/145636
2025-03-04T20:02:07.2840687Z  * [new tag]                 ciflow/inductor/145647      -> ciflow/inductor/145647
2025-03-04T20:02:07.2841417Z  * [new tag]                 ciflow/inductor/145681      -> ciflow/inductor/145681
2025-03-04T20:02:07.2842071Z  * [new tag]                 ciflow/inductor/145865      -> ciflow/inductor/145865
2025-03-04T20:02:07.2842680Z  * [new tag]                 ciflow/inductor/145885      -> ciflow/inductor/145885
2025-03-04T20:02:07.2843327Z  * [new tag]                 ciflow/inductor/145911      -> ciflow/inductor/145911
2025-03-04T20:02:07.2843967Z  * [new tag]                 ciflow/inductor/145922      -> ciflow/inductor/145922
2025-03-04T20:02:07.2844681Z  * [new tag]                 ciflow/inductor/145936      -> ciflow/inductor/145936
2025-03-04T20:02:07.2845295Z  * [new tag]                 ciflow/inductor/145966      -> ciflow/inductor/145966
2025-03-04T20:02:07.2846032Z  * [new tag]                 ciflow/inductor/145969      -> ciflow/inductor/145969
2025-03-04T20:02:07.2846707Z  * [new tag]                 ciflow/inductor/145979      -> ciflow/inductor/145979
2025-03-04T20:02:07.2847425Z  * [new tag]                 ciflow/inductor/145992      -> ciflow/inductor/145992
2025-03-04T20:02:07.2848217Z  * [new tag]                 ciflow/inductor/146051      -> ciflow/inductor/146051
2025-03-04T20:02:07.2848867Z  * [new tag]                 ciflow/inductor/146063      -> ciflow/inductor/146063
2025-03-04T20:02:07.2849529Z  * [new tag]                 ciflow/inductor/146101      -> ciflow/inductor/146101
2025-03-04T20:02:07.2850582Z  * [new tag]                 ciflow/inductor/146115      -> ciflow/inductor/146115
2025-03-04T20:02:07.2851165Z  * [new tag]                 ciflow/inductor/146135      -> ciflow/inductor/146135
2025-03-04T20:02:07.2851824Z  * [new tag]                 ciflow/inductor/146171      -> ciflow/inductor/146171
2025-03-04T20:02:07.2852489Z  * [new tag]                 ciflow/inductor/146172      -> ciflow/inductor/146172
2025-03-04T20:02:07.2853170Z  * [new tag]                 ciflow/inductor/146176      -> ciflow/inductor/146176
2025-03-04T20:02:07.2853911Z  * [new tag]                 ciflow/inductor/146180      -> ciflow/inductor/146180
2025-03-04T20:02:07.2854588Z  * [new tag]                 ciflow/inductor/146218      -> ciflow/inductor/146218
2025-03-04T20:02:07.2855702Z  * [new tag]                 ciflow/inductor/146228      -> ciflow/inductor/146228
2025-03-04T20:02:07.2856099Z  * [new tag]                 ciflow/inductor/146264      -> ciflow/inductor/146264
2025-03-04T20:02:07.2857101Z  * [new tag]                 ciflow/inductor/146267      -> ciflow/inductor/146267
2025-03-04T20:02:07.2857909Z  * [new tag]                 ciflow/inductor/146275      -> ciflow/inductor/146275
2025-03-04T20:02:07.2858656Z  * [new tag]                 ciflow/inductor/146280      -> ciflow/inductor/146280
2025-03-04T20:02:07.2859361Z  * [new tag]                 ciflow/inductor/146288      -> ciflow/inductor/146288
2025-03-04T20:02:07.2860071Z  * [new tag]                 ciflow/inductor/146319      -> ciflow/inductor/146319
2025-03-04T20:02:07.2860749Z  * [new tag]                 ciflow/inductor/146335      -> ciflow/inductor/146335
2025-03-04T20:02:07.2861394Z  * [new tag]                 ciflow/inductor/146341      -> ciflow/inductor/146341
2025-03-04T20:02:07.2862492Z  * [new tag]                 ciflow/inductor/146393      -> ciflow/inductor/146393
2025-03-04T20:02:07.2863114Z  * [new tag]                 ciflow/inductor/146395      -> ciflow/inductor/146395
2025-03-04T20:02:07.2863804Z  * [new tag]                 ciflow/inductor/146415      -> ciflow/inductor/146415
2025-03-04T20:02:07.2864443Z  * [new tag]                 ciflow/inductor/146421      -> ciflow/inductor/146421
2025-03-04T20:02:07.2865121Z  * [new tag]                 ciflow/inductor/146436      -> ciflow/inductor/146436
2025-03-04T20:02:07.2865760Z  * [new tag]                 ciflow/inductor/146455      -> ciflow/inductor/146455
2025-03-04T20:02:07.2866417Z  * [new tag]                 ciflow/inductor/146499      -> ciflow/inductor/146499
2025-03-04T20:02:07.2867097Z  * [new tag]                 ciflow/inductor/146500      -> ciflow/inductor/146500
2025-03-04T20:02:07.2867744Z  * [new tag]                 ciflow/inductor/146501      -> ciflow/inductor/146501
2025-03-04T20:02:07.2868399Z  * [new tag]                 ciflow/inductor/146502      -> ciflow/inductor/146502
2025-03-04T20:02:07.2869071Z  * [new tag]                 ciflow/inductor/146504      -> ciflow/inductor/146504
2025-03-04T20:02:07.2869749Z  * [new tag]                 ciflow/inductor/146505      -> ciflow/inductor/146505
2025-03-04T20:02:07.2870420Z  * [new tag]                 ciflow/inductor/146506      -> ciflow/inductor/146506
2025-03-04T20:02:07.2871019Z  * [new tag]                 ciflow/inductor/146526      -> ciflow/inductor/146526
2025-03-04T20:02:07.2872008Z  * [new tag]                 ciflow/inductor/146530      -> ciflow/inductor/146530
2025-03-04T20:02:07.2872614Z  * [new tag]                 ciflow/inductor/146535      -> ciflow/inductor/146535
2025-03-04T20:02:07.2873356Z  * [new tag]                 ciflow/inductor/146558      -> ciflow/inductor/146558
2025-03-04T20:02:07.2874140Z  * [new tag]                 ciflow/inductor/146561      -> ciflow/inductor/146561
2025-03-04T20:02:07.2874878Z  * [new tag]                 ciflow/inductor/146562      -> ciflow/inductor/146562
2025-03-04T20:02:07.2875547Z  * [new tag]                 ciflow/inductor/146636      -> ciflow/inductor/146636
2025-03-04T20:02:07.2876254Z  * [new tag]                 ciflow/inductor/146661      -> ciflow/inductor/146661
2025-03-04T20:02:07.2876875Z  * [new tag]                 ciflow/inductor/146678      -> ciflow/inductor/146678
2025-03-04T20:02:07.2877535Z  * [new tag]                 ciflow/inductor/146706      -> ciflow/inductor/146706
2025-03-04T20:02:07.2878200Z  * [new tag]                 ciflow/inductor/146718      -> ciflow/inductor/146718
2025-03-04T20:02:07.2878894Z  * [new tag]                 ciflow/inductor/146779      -> ciflow/inductor/146779
2025-03-04T20:02:07.2879829Z  * [new tag]                 ciflow/inductor/146781      -> ciflow/inductor/146781
2025-03-04T20:02:07.2880704Z  * [new tag]                 ciflow/inductor/146823      -> ciflow/inductor/146823
2025-03-04T20:02:07.2881416Z  * [new tag]                 ciflow/inductor/146826      -> ciflow/inductor/146826
2025-03-04T20:02:07.2882448Z  * [new tag]                 ciflow/inductor/146827      -> ciflow/inductor/146827
2025-03-04T20:02:07.2883110Z  * [new tag]                 ciflow/inductor/146844      -> ciflow/inductor/146844
2025-03-04T20:02:07.2883764Z  * [new tag]                 ciflow/inductor/146845      -> ciflow/inductor/146845
2025-03-04T20:02:07.2884444Z  * [new tag]                 ciflow/inductor/146850      -> ciflow/inductor/146850
2025-03-04T20:02:07.2885153Z  * [new tag]                 ciflow/inductor/146864      -> ciflow/inductor/146864
2025-03-04T20:02:07.2885927Z  * [new tag]                 ciflow/inductor/146870      -> ciflow/inductor/146870
2025-03-04T20:02:07.2886547Z  * [new tag]                 ciflow/inductor/146873      -> ciflow/inductor/146873
2025-03-04T20:02:07.2887577Z  * [new tag]                 ciflow/inductor/146874      -> ciflow/inductor/146874
2025-03-04T20:02:07.2888185Z  * [new tag]                 ciflow/inductor/146894      -> ciflow/inductor/146894
2025-03-04T20:02:07.2888896Z  * [new tag]                 ciflow/inductor/146895      -> ciflow/inductor/146895
2025-03-04T20:02:07.2889720Z  * [new tag]                 ciflow/inductor/146919      -> ciflow/inductor/146919
2025-03-04T20:02:07.2890388Z  * [new tag]                 ciflow/inductor/146921      -> ciflow/inductor/146921
2025-03-04T20:02:07.2891054Z  * [new tag]                 ciflow/inductor/146928      -> ciflow/inductor/146928
2025-03-04T20:02:07.2891693Z  * [new tag]                 ciflow/inductor/146935      -> ciflow/inductor/146935
2025-03-04T20:02:07.2892378Z  * [new tag]                 ciflow/inductor/146942      -> ciflow/inductor/146942
2025-03-04T20:02:07.2893118Z  * [new tag]                 ciflow/inductor/146962      -> ciflow/inductor/146962
2025-03-04T20:02:07.2893887Z  * [new tag]                 ciflow/inductor/146983      -> ciflow/inductor/146983
2025-03-04T20:02:07.2894660Z  * [new tag]                 ciflow/inductor/146989      -> ciflow/inductor/146989
2025-03-04T20:02:07.2895592Z  * [new tag]                 ciflow/inductor/147007      -> ciflow/inductor/147007
2025-03-04T20:02:07.2896240Z  * [new tag]                 ciflow/inductor/147014      -> ciflow/inductor/147014
2025-03-04T20:02:07.2896891Z  * [new tag]                 ciflow/inductor/147021      -> ciflow/inductor/147021
2025-03-04T20:02:07.2897579Z  * [new tag]                 ciflow/inductor/147036      -> ciflow/inductor/147036
2025-03-04T20:02:07.2898320Z  * [new tag]                 ciflow/inductor/147049      -> ciflow/inductor/147049
2025-03-04T20:02:07.2899008Z  * [new tag]                 ciflow/inductor/147105      -> ciflow/inductor/147105
2025-03-04T20:02:07.2899640Z  * [new tag]                 ciflow/inductor/147146      -> ciflow/inductor/147146
2025-03-04T20:02:07.2900305Z  * [new tag]                 ciflow/inductor/147149      -> ciflow/inductor/147149
2025-03-04T20:02:07.2900966Z  * [new tag]                 ciflow/inductor/147155      -> ciflow/inductor/147155
2025-03-04T20:02:07.2901654Z  * [new tag]                 ciflow/inductor/147178      -> ciflow/inductor/147178
2025-03-04T20:02:07.2902308Z  * [new tag]                 ciflow/inductor/147205      -> ciflow/inductor/147205
2025-03-04T20:02:07.2903037Z  * [new tag]                 ciflow/inductor/147225      -> ciflow/inductor/147225
2025-03-04T20:02:07.2903849Z  * [new tag]                 ciflow/inductor/147229      -> ciflow/inductor/147229
2025-03-04T20:02:07.2904489Z  * [new tag]                 ciflow/inductor/147269      -> ciflow/inductor/147269
2025-03-04T20:02:07.2905163Z  * [new tag]                 ciflow/inductor/147272      -> ciflow/inductor/147272
2025-03-04T20:02:07.2906004Z  * [new tag]                 ciflow/inductor/147314      -> ciflow/inductor/147314
2025-03-04T20:02:07.2906660Z  * [new tag]                 ciflow/inductor/147315      -> ciflow/inductor/147315
2025-03-04T20:02:07.2907363Z  * [new tag]                 ciflow/inductor/147320      -> ciflow/inductor/147320
2025-03-04T20:02:07.2908460Z  * [new tag]                 ciflow/inductor/147341      -> ciflow/inductor/147341
2025-03-04T20:02:07.2909214Z  * [new tag]                 ciflow/inductor/147360      -> ciflow/inductor/147360
2025-03-04T20:02:07.2909907Z  * [new tag]                 ciflow/inductor/147368      -> ciflow/inductor/147368
2025-03-04T20:02:07.2910579Z  * [new tag]                 ciflow/inductor/147403      -> ciflow/inductor/147403
2025-03-04T20:02:07.2911203Z  * [new tag]                 ciflow/inductor/147410      -> ciflow/inductor/147410
2025-03-04T20:02:07.2911883Z  * [new tag]                 ciflow/inductor/147414      -> ciflow/inductor/147414
2025-03-04T20:02:07.2912583Z  * [new tag]                 ciflow/inductor/147415      -> ciflow/inductor/147415
2025-03-04T20:02:07.2913293Z  * [new tag]                 ciflow/inductor/147422      -> ciflow/inductor/147422
2025-03-04T20:02:07.2914296Z  * [new tag]                 ciflow/inductor/147445      -> ciflow/inductor/147445
2025-03-04T20:02:07.2914991Z  * [new tag]                 ciflow/inductor/147452      -> ciflow/inductor/147452
2025-03-04T20:02:07.2915635Z  * [new tag]                 ciflow/inductor/147481      -> ciflow/inductor/147481
2025-03-04T20:02:07.2916342Z  * [new tag]                 ciflow/inductor/147485      -> ciflow/inductor/147485
2025-03-04T20:02:07.2917012Z  * [new tag]                 ciflow/inductor/147498      -> ciflow/inductor/147498
2025-03-04T20:02:07.2917684Z  * [new tag]                 ciflow/inductor/147514      -> ciflow/inductor/147514
2025-03-04T20:02:07.2918363Z  * [new tag]                 ciflow/inductor/147528      -> ciflow/inductor/147528
2025-03-04T20:02:07.2919047Z  * [new tag]                 ciflow/inductor/147552      -> ciflow/inductor/147552
2025-03-04T20:02:07.2919725Z  * [new tag]                 ciflow/inductor/147557      -> ciflow/inductor/147557
2025-03-04T20:02:07.2920397Z  * [new tag]                 ciflow/inductor/147561      -> ciflow/inductor/147561
2025-03-04T20:02:07.2921066Z  * [new tag]                 ciflow/inductor/147562      -> ciflow/inductor/147562
2025-03-04T20:02:07.2921782Z  * [new tag]                 ciflow/inductor/147574      -> ciflow/inductor/147574
2025-03-04T20:02:07.2922410Z  * [new tag]                 ciflow/inductor/147583      -> ciflow/inductor/147583
2025-03-04T20:02:07.2923079Z  * [new tag]                 ciflow/inductor/147592      -> ciflow/inductor/147592
2025-03-04T20:02:07.2923746Z  * [new tag]                 ciflow/inductor/147603      -> ciflow/inductor/147603
2025-03-04T20:02:07.2924488Z  * [new tag]                 ciflow/inductor/147619      -> ciflow/inductor/147619
2025-03-04T20:02:07.2925119Z  * [new tag]                 ciflow/inductor/147648      -> ciflow/inductor/147648
2025-03-04T20:02:07.2925789Z  * [new tag]                 ciflow/inductor/147660      -> ciflow/inductor/147660
2025-03-04T20:02:07.2926531Z  * [new tag]                 ciflow/inductor/147727      -> ciflow/inductor/147727
2025-03-04T20:02:07.2927277Z  * [new tag]                 ciflow/inductor/147741      -> ciflow/inductor/147741
2025-03-04T20:02:07.2927883Z  * [new tag]                 ciflow/inductor/147745      -> ciflow/inductor/147745
2025-03-04T20:02:07.2928690Z  * [new tag]                 ciflow/inductor/147768      -> ciflow/inductor/147768
2025-03-04T20:02:07.2929364Z  * [new tag]                 ciflow/inductor/147790      -> ciflow/inductor/147790
2025-03-04T20:02:07.2930017Z  * [new tag]                 ciflow/inductor/147797      -> ciflow/inductor/147797
2025-03-04T20:02:07.2930708Z  * [new tag]                 ciflow/inductor/147798      -> ciflow/inductor/147798
2025-03-04T20:02:07.2931369Z  * [new tag]                 ciflow/inductor/147800      -> ciflow/inductor/147800
2025-03-04T20:02:07.2932078Z  * [new tag]                 ciflow/inductor/147817      -> ciflow/inductor/147817
2025-03-04T20:02:07.2932838Z  * [new tag]                 ciflow/inductor/147821      -> ciflow/inductor/147821
2025-03-04T20:02:07.2933521Z  * [new tag]                 ciflow/inductor/147836      -> ciflow/inductor/147836
2025-03-04T20:02:07.2934313Z  * [new tag]                 ciflow/inductor/147863      -> ciflow/inductor/147863
2025-03-04T20:02:07.2935086Z  * [new tag]                 ciflow/inductor/147870      -> ciflow/inductor/147870
2025-03-04T20:02:07.2935713Z  * [new tag]                 ciflow/inductor/147881      -> ciflow/inductor/147881
2025-03-04T20:02:07.2936508Z  * [new tag]                 ciflow/inductor/147899      -> ciflow/inductor/147899
2025-03-04T20:02:07.2937157Z  * [new tag]                 ciflow/inductor/147902      -> ciflow/inductor/147902
2025-03-04T20:02:07.2937907Z  * [new tag]                 ciflow/inductor/147903      -> ciflow/inductor/147903
2025-03-04T20:02:07.2938952Z  * [new tag]                 ciflow/inductor/147908      -> ciflow/inductor/147908
2025-03-04T20:02:07.2939568Z  * [new tag]                 ciflow/inductor/147910      -> ciflow/inductor/147910
2025-03-04T20:02:07.2940215Z  * [new tag]                 ciflow/inductor/147915      -> ciflow/inductor/147915
2025-03-04T20:02:07.2940896Z  * [new tag]                 ciflow/inductor/147917      -> ciflow/inductor/147917
2025-03-04T20:02:07.2941686Z  * [new tag]                 ciflow/inductor/147927      -> ciflow/inductor/147927
2025-03-04T20:02:07.2942360Z  * [new tag]                 ciflow/inductor/147945      -> ciflow/inductor/147945
2025-03-04T20:02:07.2943167Z  * [new tag]                 ciflow/inductor/147955      -> ciflow/inductor/147955
2025-03-04T20:02:07.2943973Z  * [new tag]                 ciflow/inductor/147956      -> ciflow/inductor/147956
2025-03-04T20:02:07.2944794Z  * [new tag]                 ciflow/inductor/147957      -> ciflow/inductor/147957
2025-03-04T20:02:07.2945635Z  * [new tag]                 ciflow/inductor/147958      -> ciflow/inductor/147958
2025-03-04T20:02:07.2946398Z  * [new tag]                 ciflow/inductor/147959      -> ciflow/inductor/147959
2025-03-04T20:02:07.2947241Z  * [new tag]                 ciflow/inductor/147960      -> ciflow/inductor/147960
2025-03-04T20:02:07.2947930Z  * [new tag]                 ciflow/inductor/147962      -> ciflow/inductor/147962
2025-03-04T20:02:07.2948579Z  * [new tag]                 ciflow/inductor/147990      -> ciflow/inductor/147990
2025-03-04T20:02:07.2949319Z  * [new tag]                 ciflow/inductor/148002      -> ciflow/inductor/148002
2025-03-04T20:02:07.2950300Z  * [new tag]                 ciflow/inductor/148007      -> ciflow/inductor/148007
2025-03-04T20:02:07.2950926Z  * [new tag]                 ciflow/inductor/148008      -> ciflow/inductor/148008
2025-03-04T20:02:07.2951682Z  * [new tag]                 ciflow/inductor/148010      -> ciflow/inductor/148010
2025-03-04T20:02:07.2952411Z  * [new tag]                 ciflow/inductor/148042      -> ciflow/inductor/148042
2025-03-04T20:02:07.2953278Z  * [new tag]                 ciflow/inductor/148046      -> ciflow/inductor/148046
2025-03-04T20:02:07.2953900Z  * [new tag]                 ciflow/inductor/148063      -> ciflow/inductor/148063
2025-03-04T20:02:07.2955107Z  * [new tag]                 ciflow/inductor/148083      -> ciflow/inductor/148083
2025-03-04T20:02:07.2955852Z  * [new tag]                 ciflow/inductor/148091      -> ciflow/inductor/148091
2025-03-04T20:02:07.2956659Z  * [new tag]                 ciflow/inductor/148092      -> ciflow/inductor/148092
2025-03-04T20:02:07.2957326Z  * [new tag]                 ciflow/inductor/148104      -> ciflow/inductor/148104
2025-03-04T20:02:07.2958056Z  * [new tag]                 ciflow/inductor/148130      -> ciflow/inductor/148130
2025-03-04T20:02:07.2958793Z  * [new tag]                 ciflow/inductor/148131      -> ciflow/inductor/148131
2025-03-04T20:02:07.2959785Z  * [new tag]                 ciflow/inductor/148132      -> ciflow/inductor/148132
2025-03-04T20:02:07.2960494Z  * [new tag]                 ciflow/inductor/148138      -> ciflow/inductor/148138
2025-03-04T20:02:07.2961214Z  * [new tag]                 ciflow/inductor/148139      -> ciflow/inductor/148139
2025-03-04T20:02:07.2961953Z  * [new tag]                 ciflow/inductor/148160      -> ciflow/inductor/148160
2025-03-04T20:02:07.2963081Z  * [new tag]                 ciflow/inductor/148163      -> ciflow/inductor/148163
2025-03-04T20:02:07.2963615Z  * [new tag]                 ciflow/inductor/148173      -> ciflow/inductor/148173
2025-03-04T20:02:07.2964329Z  * [new tag]                 ciflow/inductor/148174      -> ciflow/inductor/148174
2025-03-04T20:02:07.2965087Z  * [new tag]                 ciflow/inductor/148176      -> ciflow/inductor/148176
2025-03-04T20:02:07.2965836Z  * [new tag]                 ciflow/inductor/148186      -> ciflow/inductor/148186
2025-03-04T20:02:07.2966823Z  * [new tag]                 ciflow/inductor/148190      -> ciflow/inductor/148190
2025-03-04T20:02:07.2967548Z  * [new tag]                 ciflow/inductor/148202      -> ciflow/inductor/148202
2025-03-04T20:02:07.2968286Z  * [new tag]                 ciflow/inductor/148205      -> ciflow/inductor/148205
2025-03-04T20:02:07.2969080Z  * [new tag]                 ciflow/inductor/148206      -> ciflow/inductor/148206
2025-03-04T20:02:07.2969781Z  * [new tag]                 ciflow/inductor/148209      -> ciflow/inductor/148209
2025-03-04T20:02:07.2970529Z  * [new tag]                 ciflow/inductor/148210      -> ciflow/inductor/148210
2025-03-04T20:02:07.2971303Z  * [new tag]                 ciflow/inductor/148212      -> ciflow/inductor/148212
2025-03-04T20:02:07.2972039Z  * [new tag]                 ciflow/inductor/148220      -> ciflow/inductor/148220
2025-03-04T20:02:07.2973038Z  * [new tag]                 ciflow/inductor/148223      -> ciflow/inductor/148223
2025-03-04T20:02:07.2974141Z  * [new tag]                 ciflow/inductor/148231      -> ciflow/inductor/148231
2025-03-04T20:02:07.2974885Z  * [new tag]                 ciflow/inductor/148233      -> ciflow/inductor/148233
2025-03-04T20:02:07.2975716Z  * [new tag]                 ciflow/inductor/148234      -> ciflow/inductor/148234
2025-03-04T20:02:07.2976430Z  * [new tag]                 ciflow/inductor/148235      -> ciflow/inductor/148235
2025-03-04T20:02:07.2977307Z  * [new tag]                 ciflow/inductor/148236      -> ciflow/inductor/148236
2025-03-04T20:02:07.2978122Z  * [new tag]                 ciflow/inductor/148243      -> ciflow/inductor/148243
2025-03-04T20:02:07.2978837Z  * [new tag]                 ciflow/inductor/148260      -> ciflow/inductor/148260
2025-03-04T20:02:07.2979598Z  * [new tag]                 ciflow/inductor/148261      -> ciflow/inductor/148261
2025-03-04T20:02:07.2980334Z  * [new tag]                 ciflow/inductor/148279      -> ciflow/inductor/148279
2025-03-04T20:02:07.2981128Z  * [new tag]                 ciflow/inductor/148288      -> ciflow/inductor/148288
2025-03-04T20:02:07.2982067Z  * [new tag]                 ciflow/inductor/148290      -> ciflow/inductor/148290
2025-03-04T20:02:07.2982774Z  * [new tag]                 ciflow/inductor/148292      -> ciflow/inductor/148292
2025-03-04T20:02:07.2983728Z  * [new tag]                 ciflow/inductor/148294      -> ciflow/inductor/148294
2025-03-04T20:02:07.2984875Z  * [new tag]                 ciflow/inductor/148303      -> ciflow/inductor/148303
2025-03-04T20:02:07.2985235Z  * [new tag]                 ciflow/inductor/148305      -> ciflow/inductor/148305
2025-03-04T20:02:07.2985898Z  * [new tag]                 ciflow/inductor/148323      -> ciflow/inductor/148323
2025-03-04T20:02:07.2986629Z  * [new tag]                 ciflow/inductor/148328      -> ciflow/inductor/148328
2025-03-04T20:02:07.2987349Z  * [new tag]                 ciflow/inductor/148357      -> ciflow/inductor/148357
2025-03-04T20:02:07.2988102Z  * [new tag]                 ciflow/inductor/148358      -> ciflow/inductor/148358
2025-03-04T20:02:07.2988813Z  * [new tag]                 ciflow/inductor/148359      -> ciflow/inductor/148359
2025-03-04T20:02:07.2989853Z  * [new tag]                 ciflow/inductor/148363      -> ciflow/inductor/148363
2025-03-04T20:02:07.2990513Z  * [new tag]                 ciflow/inductor/148364      -> ciflow/inductor/148364
2025-03-04T20:02:07.2991405Z  * [new tag]                 ciflow/inductor/148366      -> ciflow/inductor/148366
2025-03-04T20:02:07.2992078Z  * [new tag]                 ciflow/inductor/148367      -> ciflow/inductor/148367
2025-03-04T20:02:07.2992968Z  * [new tag]                 ciflow/inductor/148376      -> ciflow/inductor/148376
2025-03-04T20:02:07.2993620Z  * [new tag]                 ciflow/inductor/148377      -> ciflow/inductor/148377
2025-03-04T20:02:07.2994675Z  * [new tag]                 ciflow/inductor/148380      -> ciflow/inductor/148380
2025-03-04T20:02:07.2995346Z  * [new tag]                 ciflow/inductor/148381      -> ciflow/inductor/148381
2025-03-04T20:02:07.2996058Z  * [new tag]                 ciflow/inductor/148385      -> ciflow/inductor/148385
2025-03-04T20:02:07.2997000Z  * [new tag]                 ciflow/inductor/148386      -> ciflow/inductor/148386
2025-03-04T20:02:07.2997509Z  * [new tag]                 ciflow/inductor/148401      -> ciflow/inductor/148401
2025-03-04T20:02:07.2998499Z  * [new tag]                 ciflow/inductor/148407      -> ciflow/inductor/148407
2025-03-04T20:02:07.2999154Z  * [new tag]                 ciflow/inductor/148413      -> ciflow/inductor/148413
2025-03-04T20:02:07.2999967Z  * [new tag]                 ciflow/inductor/148414      -> ciflow/inductor/148414
2025-03-04T20:02:07.3000842Z  * [new tag]                 ciflow/inductor/148415      -> ciflow/inductor/148415
2025-03-04T20:02:07.3001712Z  * [new tag]                 ciflow/inductor/148418      -> ciflow/inductor/148418
2025-03-04T20:02:07.3002441Z  * [new tag]                 ciflow/inductor/148423      -> ciflow/inductor/148423
2025-03-04T20:02:07.3003311Z  * [new tag]                 ciflow/inductor/148424      -> ciflow/inductor/148424
2025-03-04T20:02:07.3003959Z  * [new tag]                 ciflow/inductor/148430      -> ciflow/inductor/148430
2025-03-04T20:02:07.3004981Z  * [new tag]                 ciflow/inductor/148432      -> ciflow/inductor/148432
2025-03-04T20:02:07.3006563Z  * [new tag]                 ciflow/inductor/148445      -> ciflow/inductor/148445
2025-03-04T20:02:07.3007274Z  * [new tag]                 ciflow/inductor/148450      -> ciflow/inductor/148450
2025-03-04T20:02:07.3008016Z  * [new tag]                 ciflow/inductor/148454      -> ciflow/inductor/148454
2025-03-04T20:02:07.3008739Z  * [new tag]                 ciflow/inductor/148459      -> ciflow/inductor/148459
2025-03-04T20:02:07.3009644Z  * [new tag]                 ciflow/inductor/148470      -> ciflow/inductor/148470
2025-03-04T20:02:07.3010310Z  * [new tag]                 ciflow/inductor/148473      -> ciflow/inductor/148473
2025-03-04T20:02:07.3011322Z  * [new tag]                 ciflow/inductor/3b9a386     -> ciflow/inductor/3b9a386
2025-03-04T20:02:07.3012234Z  * [new tag]                 ciflow/inductor/3d4b92b     -> ciflow/inductor/3d4b92b
2025-03-04T20:02:07.3013110Z  * [new tag]                 ciflow/inductor/88106       -> ciflow/inductor/88106
2025-03-04T20:02:07.3014046Z  * [new tag]                 ciflow/inductor/88196       -> ciflow/inductor/88196
2025-03-04T20:02:07.3014954Z  * [new tag]                 ciflow/inductor/88998       -> ciflow/inductor/88998
2025-03-04T20:02:07.3015950Z  * [new tag]                 ciflow/inductor/d224ac7     -> ciflow/inductor/d224ac7
2025-03-04T20:02:07.3016662Z  * [new tag]                 ciflow/linux-aarch64/125888 -> ciflow/linux-aarch64/125888
2025-03-04T20:02:07.3017288Z  * [new tag]                 ciflow/linux-aarch64/126050 -> ciflow/linux-aarch64/126050
2025-03-04T20:02:07.3017973Z  * [new tag]                 ciflow/linux-aarch64/126054 -> ciflow/linux-aarch64/126054
2025-03-04T20:02:07.3018633Z  * [new tag]                 ciflow/linux-aarch64/133297 -> ciflow/linux-aarch64/133297
2025-03-04T20:02:07.3019231Z  * [new tag]                 ciflow/linux-aarch64/133315 -> ciflow/linux-aarch64/133315
2025-03-04T20:02:07.3019851Z  * [new tag]                 ciflow/linux-aarch64/133392 -> ciflow/linux-aarch64/133392
2025-03-04T20:02:07.3020501Z  * [new tag]                 ciflow/linux-aarch64/133419 -> ciflow/linux-aarch64/133419
2025-03-04T20:02:07.3021161Z  * [new tag]                 ciflow/linux-aarch64/133423 -> ciflow/linux-aarch64/133423
2025-03-04T20:02:07.3021714Z  * [new tag]                 ciflow/linux-aarch64/133667 -> ciflow/linux-aarch64/133667
2025-03-04T20:02:07.3022363Z  * [new tag]                 ciflow/linux-aarch64/133753 -> ciflow/linux-aarch64/133753
2025-03-04T20:02:07.3023140Z  * [new tag]                 ciflow/linux-aarch64/135058 -> ciflow/linux-aarch64/135058
2025-03-04T20:02:07.3024179Z  * [new tag]                 ciflow/linux-aarch64/135333 -> ciflow/linux-aarch64/135333
2025-03-04T20:02:07.3024920Z  * [new tag]                 ciflow/linux-aarch64/135792 -> ciflow/linux-aarch64/135792
2025-03-04T20:02:07.3025884Z  * [new tag]                 ciflow/linux-aarch64/136355 -> ciflow/linux-aarch64/136355
2025-03-04T20:02:07.3026474Z  * [new tag]                 ciflow/linux-aarch64/137568 -> ciflow/linux-aarch64/137568
2025-03-04T20:02:07.3027178Z  * [new tag]                 ciflow/linux-aarch64/138388 -> ciflow/linux-aarch64/138388
2025-03-04T20:02:07.3027698Z  * [new tag]                 ciflow/linux-aarch64/138889 -> ciflow/linux-aarch64/138889
2025-03-04T20:02:07.3028392Z  * [new tag]                 ciflow/linux-aarch64/140159 -> ciflow/linux-aarch64/140159
2025-03-04T20:02:07.3029276Z  * [new tag]                 ciflow/linux-aarch64/143741 -> ciflow/linux-aarch64/143741
2025-03-04T20:02:07.3030230Z  * [new tag]                 ciflow/linux-aarch64/145942 -> ciflow/linux-aarch64/145942
2025-03-04T20:02:07.3030809Z  * [new tag]                 ciflow/linux-aarch64/146823 -> ciflow/linux-aarch64/146823
2025-03-04T20:02:07.3031455Z  * [new tag]                 ciflow/linux-aarch64/146826 -> ciflow/linux-aarch64/146826
2025-03-04T20:02:07.3032100Z  * [new tag]                 ciflow/linux-aarch64/146895 -> ciflow/linux-aarch64/146895
2025-03-04T20:02:07.3032784Z  * [new tag]                 ciflow/linux-aarch64/147073 -> ciflow/linux-aarch64/147073
2025-03-04T20:02:07.3033608Z  * [new tag]                 ciflow/linux-aarch64/147337 -> ciflow/linux-aarch64/147337
2025-03-04T20:02:07.3034236Z  * [new tag]                 ciflow/linux-aarch64/147341 -> ciflow/linux-aarch64/147341
2025-03-04T20:02:07.3034886Z  * [new tag]                 ciflow/linux-aarch64/147359 -> ciflow/linux-aarch64/147359
2025-03-04T20:02:07.3035510Z  * [new tag]                 ciflow/linux-aarch64/147498 -> ciflow/linux-aarch64/147498
2025-03-04T20:02:07.3036412Z  * [new tag]                 ciflow/linux-aarch64/147763 -> ciflow/linux-aarch64/147763
2025-03-04T20:02:07.3036910Z  * [new tag]                 ciflow/linux-aarch64/147817 -> ciflow/linux-aarch64/147817
2025-03-04T20:02:07.3037582Z  * [new tag]                 ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855
2025-03-04T20:02:07.3038238Z  * [new tag]                 ciflow/linux-aarch64/147917 -> ciflow/linux-aarch64/147917
2025-03-04T20:02:07.3038902Z  * [new tag]                 ciflow/linux-aarch64/147945 -> ciflow/linux-aarch64/147945
2025-03-04T20:02:07.3039544Z  * [new tag]                 ciflow/linux-aarch64/147955 -> ciflow/linux-aarch64/147955
2025-03-04T20:02:07.3040190Z  * [new tag]                 ciflow/linux-aarch64/147956 -> ciflow/linux-aarch64/147956
2025-03-04T20:02:07.3040829Z  * [new tag]                 ciflow/linux-aarch64/147957 -> ciflow/linux-aarch64/147957
2025-03-04T20:02:07.3041460Z  * [new tag]                 ciflow/linux-aarch64/147958 -> ciflow/linux-aarch64/147958
2025-03-04T20:02:07.3042127Z  * [new tag]                 ciflow/linux-aarch64/147959 -> ciflow/linux-aarch64/147959
2025-03-04T20:02:07.3042755Z  * [new tag]                 ciflow/linux-aarch64/147964 -> ciflow/linux-aarch64/147964
2025-03-04T20:02:07.3043524Z  * [new tag]                 ciflow/linux-aarch64/148076 -> ciflow/linux-aarch64/148076
2025-03-04T20:02:07.3044173Z  * [new tag]                 ciflow/linux-aarch64/148163 -> ciflow/linux-aarch64/148163
2025-03-04T20:02:07.3044802Z  * [new tag]                 ciflow/linux-aarch64/148173 -> ciflow/linux-aarch64/148173
2025-03-04T20:02:07.3045482Z  * [new tag]                 ciflow/linux-aarch64/148403 -> ciflow/linux-aarch64/148403
2025-03-04T20:02:07.3046498Z  * [new tag]                 ciflow/mps/102148           -> ciflow/mps/102148
2025-03-04T20:02:07.3047139Z  * [new tag]                 ciflow/mps/119496           -> ciflow/mps/119496
2025-03-04T20:02:07.3047744Z  * [new tag]                 ciflow/mps/120076           -> ciflow/mps/120076
2025-03-04T20:02:07.3048372Z  * [new tag]                 ciflow/mps/133423           -> ciflow/mps/133423
2025-03-04T20:02:07.3048953Z  * [new tag]                 ciflow/mps/133667           -> ciflow/mps/133667
2025-03-04T20:02:07.3049928Z  * [new tag]                 ciflow/mps/138640           -> ciflow/mps/138640
2025-03-04T20:02:07.3050545Z  * [new tag]                 ciflow/mps/139469           -> ciflow/mps/139469
2025-03-04T20:02:07.3051189Z  * [new tag]                 ciflow/mps/140159           -> ciflow/mps/140159
2025-03-04T20:02:07.3051901Z  * [new tag]                 ciflow/mps/140211           -> ciflow/mps/140211
2025-03-04T20:02:07.3053081Z  * [new tag]                 ciflow/mps/140725           -> ciflow/mps/140725
2025-03-04T20:02:07.3053732Z  * [new tag]                 ciflow/mps/142097           -> ciflow/mps/142097
2025-03-04T20:02:07.3054851Z  * [new tag]                 ciflow/mps/142202           -> ciflow/mps/142202
2025-03-04T20:02:07.3055794Z  * [new tag]                 ciflow/mps/142477           -> ciflow/mps/142477
2025-03-04T20:02:07.3056526Z  * [new tag]                 ciflow/mps/143630           -> ciflow/mps/143630
2025-03-04T20:02:07.3057161Z  * [new tag]                 ciflow/mps/143666           -> ciflow/mps/143666
2025-03-04T20:02:07.3057859Z  * [new tag]                 ciflow/mps/143911           -> ciflow/mps/143911
2025-03-04T20:02:07.3058561Z  * [new tag]                 ciflow/mps/143966           -> ciflow/mps/143966
2025-03-04T20:02:07.3059240Z  * [new tag]                 ciflow/mps/144405           -> ciflow/mps/144405
2025-03-04T20:02:07.3059881Z  * [new tag]                 ciflow/mps/144664           -> ciflow/mps/144664
2025-03-04T20:02:07.3061155Z  * [new tag]                 ciflow/mps/145955           -> ciflow/mps/145955
2025-03-04T20:02:07.3062235Z  * [new tag]                 ciflow/mps/146098           -> ciflow/mps/146098
2025-03-04T20:02:07.3062863Z  * [new tag]                 ciflow/mps/146436           -> ciflow/mps/146436
2025-03-04T20:02:07.3063767Z  * [new tag]                 ciflow/mps/146754           -> ciflow/mps/146754
2025-03-04T20:02:07.3064223Z  * [new tag]                 ciflow/mps/146989           -> ciflow/mps/146989
2025-03-04T20:02:07.3064885Z  * [new tag]                 ciflow/mps/147205           -> ciflow/mps/147205
2025-03-04T20:02:07.3065588Z  * [new tag]                 ciflow/mps/147583           -> ciflow/mps/147583
2025-03-04T20:02:07.3066559Z  * [new tag]                 ciflow/mps/147644           -> ciflow/mps/147644
2025-03-04T20:02:07.3067154Z  * [new tag]                 ciflow/mps/147893           -> ciflow/mps/147893
2025-03-04T20:02:07.3067768Z  * [new tag]                 ciflow/mps/148305           -> ciflow/mps/148305
2025-03-04T20:02:07.3068640Z  * [new tag]                 ciflow/mps/148350           -> ciflow/mps/148350
2025-03-04T20:02:07.3069125Z  * [new tag]                 ciflow/mps/148415           -> ciflow/mps/148415
2025-03-04T20:02:07.3069805Z  * [new tag]                 ciflow/mps/148449           -> ciflow/mps/148449
2025-03-04T20:02:07.3070453Z  * [new tag]                 ciflow/mps/148468           -> ciflow/mps/148468
2025-03-04T20:02:07.3071361Z  * [new tag]                 ciflow/mps/148471           -> ciflow/mps/148471
2025-03-04T20:02:07.3072251Z  * [new tag]                 ciflow/op-benchmark/143733  -> ciflow/op-benchmark/143733
2025-03-04T20:02:07.3073190Z  * [new tag]                 ciflow/periodic/054a2fd     -> ciflow/periodic/054a2fd
2025-03-04T20:02:07.3074097Z  * [new tag]                 ciflow/periodic/123020      -> ciflow/periodic/123020
2025-03-04T20:02:07.3075134Z  * [new tag]                 ciflow/periodic/134817      -> ciflow/periodic/134817
2025-03-04T20:02:07.3075510Z  * [new tag]                 ciflow/periodic/140989      -> ciflow/periodic/140989
2025-03-04T20:02:07.3076131Z  * [new tag]                 ciflow/periodic/141309      -> ciflow/periodic/141309
2025-03-04T20:02:07.3076741Z  * [new tag]                 ciflow/periodic/141355      -> ciflow/periodic/141355
2025-03-04T20:02:07.3077548Z  * [new tag]                 ciflow/periodic/141730      -> ciflow/periodic/141730
2025-03-04T20:02:07.3078254Z  * [new tag]                 ciflow/periodic/142179      -> ciflow/periodic/142179
2025-03-04T20:02:07.3078889Z  * [new tag]                 ciflow/periodic/143959      -> ciflow/periodic/143959
2025-03-04T20:02:07.3079497Z  * [new tag]                 ciflow/periodic/144953      -> ciflow/periodic/144953
2025-03-04T20:02:07.3080085Z  * [new tag]                 ciflow/periodic/146264      -> ciflow/periodic/146264
2025-03-04T20:02:07.3081150Z  * [new tag]                 ciflow/periodic/146403      -> ciflow/periodic/146403
2025-03-04T20:02:07.3082282Z  * [new tag]                 ciflow/periodic/146823      -> ciflow/periodic/146823
2025-03-04T20:02:07.3083052Z  * [new tag]                 ciflow/periodic/146903      -> ciflow/periodic/146903
2025-03-04T20:02:07.3083922Z  * [new tag]                 ciflow/periodic/147459      -> ciflow/periodic/147459
2025-03-04T20:02:07.3084474Z  * [new tag]                 ciflow/periodic/147870      -> ciflow/periodic/147870
2025-03-04T20:02:07.3085117Z  * [new tag]                 ciflow/periodic/148351      -> ciflow/periodic/148351
2025-03-04T20:02:07.3086045Z  * [new tag]                 ciflow/periodic/2a6d37d     -> ciflow/periodic/2a6d37d
2025-03-04T20:02:07.3086781Z  * [new tag]                 ciflow/periodic/317eeb8     -> ciflow/periodic/317eeb8
2025-03-04T20:02:07.3087673Z  * [new tag]                 ciflow/periodic/3c32        -> ciflow/periodic/3c32
2025-03-04T20:02:07.3088441Z  * [new tag]                 ciflow/periodic/3e98831     -> ciflow/periodic/3e98831
2025-03-04T20:02:07.3089394Z  * [new tag]                 ciflow/periodic/94512-point -> ciflow/periodic/94512-point
2025-03-04T20:02:07.3090450Z  * [new tag]                 ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519
2025-03-04T20:02:07.3091214Z  * [new tag]                 ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275
2025-03-04T20:02:07.3091985Z  * [new tag]                 ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761
2025-03-04T20:02:07.3092921Z  * [new tag]                 ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12
2025-03-04T20:02:07.3093860Z  * [new tag]                 ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0
2025-03-04T20:02:07.3094886Z  * [new tag]                 ciflow/periodic/sha-ec5b83  -> ciflow/periodic/sha-ec5b83
2025-03-04T20:02:07.3095759Z  * [new tag]                 ciflow/riscv64/143979       -> ciflow/riscv64/143979
2025-03-04T20:02:07.3096665Z  * [new tag]                 ciflow/rocm/124424          -> ciflow/rocm/124424
2025-03-04T20:02:07.3097187Z  * [new tag]                 ciflow/rocm/134817          -> ciflow/rocm/134817
2025-03-04T20:02:07.3098162Z  * [new tag]                 ciflow/rocm/137136          -> ciflow/rocm/137136
2025-03-04T20:02:07.3098792Z  * [new tag]                 ciflow/rocm/139469          -> ciflow/rocm/139469
2025-03-04T20:02:07.3099359Z  * [new tag]                 ciflow/rocm/139975          -> ciflow/rocm/139975
2025-03-04T20:02:07.3100000Z  * [new tag]                 ciflow/rocm/140989          -> ciflow/rocm/140989
2025-03-04T20:02:07.3100593Z  * [new tag]                 ciflow/rocm/141309          -> ciflow/rocm/141309
2025-03-04T20:02:07.3101199Z  * [new tag]                 ciflow/rocm/141355          -> ciflow/rocm/141355
2025-03-04T20:02:07.3101828Z  * [new tag]                 ciflow/rocm/142097          -> ciflow/rocm/142097
2025-03-04T20:02:07.3102509Z  * [new tag]                 ciflow/rocm/142859          -> ciflow/rocm/142859
2025-03-04T20:02:07.3103095Z  * [new tag]                 ciflow/rocm/143416          -> ciflow/rocm/143416
2025-03-04T20:02:07.3104142Z  * [new tag]                 ciflow/rocm/143971          -> ciflow/rocm/143971
2025-03-04T20:02:07.3104990Z  * [new tag]                 ciflow/rocm/144120          -> ciflow/rocm/144120
2025-03-04T20:02:07.3105924Z  * [new tag]                 ciflow/rocm/144572          -> ciflow/rocm/144572
2025-03-04T20:02:07.3106529Z  * [new tag]                 ciflow/rocm/144664          -> ciflow/rocm/144664
2025-03-04T20:02:07.3107210Z  * [new tag]                 ciflow/rocm/145475          -> ciflow/rocm/145475
2025-03-04T20:02:07.3107932Z  * [new tag]                 ciflow/rocm/145584          -> ciflow/rocm/145584
2025-03-04T20:02:07.3108593Z  * [new tag]                 ciflow/rocm/145685          -> ciflow/rocm/145685
2025-03-04T20:02:07.3109431Z  * [new tag]                 ciflow/rocm/145946          -> ciflow/rocm/145946
2025-03-04T20:02:07.3110143Z  * [new tag]                 ciflow/rocm/146227          -> ciflow/rocm/146227
2025-03-04T20:02:07.3110757Z  * [new tag]                 ciflow/rocm/146264          -> ciflow/rocm/146264
2025-03-04T20:02:07.3111664Z  * [new tag]                 ciflow/rocm/146448          -> ciflow/rocm/146448
2025-03-04T20:02:07.3112273Z  * [new tag]                 ciflow/rocm/146903          -> ciflow/rocm/146903
2025-03-04T20:02:07.3112998Z  * [new tag]                 ciflow/rocm/147034          -> ciflow/rocm/147034
2025-03-04T20:02:07.3113843Z  * [new tag]                 ciflow/rocm/147243          -> ciflow/rocm/147243
2025-03-04T20:02:07.3114362Z  * [new tag]                 ciflow/rocm/147315          -> ciflow/rocm/147315
2025-03-04T20:02:07.3115029Z  * [new tag]                 ciflow/rocm/147320          -> ciflow/rocm/147320
2025-03-04T20:02:07.3115941Z  * [new tag]                 ciflow/rocm/147382          -> ciflow/rocm/147382
2025-03-04T20:02:07.3116656Z  * [new tag]                 ciflow/rocm/147403          -> ciflow/rocm/147403
2025-03-04T20:02:07.3117259Z  * [new tag]                 ciflow/rocm/147452          -> ciflow/rocm/147452
2025-03-04T20:02:07.3117905Z  * [new tag]                 ciflow/rocm/147459          -> ciflow/rocm/147459
2025-03-04T20:02:07.3118752Z  * [new tag]                 ciflow/rocm/147527          -> ciflow/rocm/147527
2025-03-04T20:02:07.3119374Z  * [new tag]                 ciflow/rocm/147619          -> ciflow/rocm/147619
2025-03-04T20:02:07.3120097Z  * [new tag]                 ciflow/rocm/147630          -> ciflow/rocm/147630
2025-03-04T20:02:07.3120802Z  * [new tag]                 ciflow/rocm/147821          -> ciflow/rocm/147821
2025-03-04T20:02:07.3121432Z  * [new tag]                 ciflow/rocm/147904          -> ciflow/rocm/147904
2025-03-04T20:02:07.3122183Z  * [new tag]                 ciflow/rocm/147993          -> ciflow/rocm/147993
2025-03-04T20:02:07.3122810Z  * [new tag]                 ciflow/rocm/148223          -> ciflow/rocm/148223
2025-03-04T20:02:07.3124107Z  * [new tag]                 ciflow/rocm/148228          -> ciflow/rocm/148228
2025-03-04T20:02:07.3124829Z  * [new tag]                 ciflow/rocm/148371          -> ciflow/rocm/148371
2025-03-04T20:02:07.3125460Z  * [new tag]                 ciflow/rocm/148394          -> ciflow/rocm/148394
2025-03-04T20:02:07.3126101Z  * [new tag]                 ciflow/rocm/148432          -> ciflow/rocm/148432
2025-03-04T20:02:07.3126748Z  * [new tag]                 ciflow/rocm/148437          -> ciflow/rocm/148437
2025-03-04T20:02:07.3127704Z  * [new tag]                 ciflow/s390/142346          -> ciflow/s390/142346
2025-03-04T20:02:07.3128311Z  * [new tag]                 ciflow/s390/143959          -> ciflow/s390/143959
2025-03-04T20:02:07.3129000Z  * [new tag]                 ciflow/s390/148452          -> ciflow/s390/148452
2025-03-04T20:02:07.3130025Z  * [new tag]                 ciflow/slow/01c7106         -> ciflow/slow/01c7106
2025-03-04T20:02:07.3130773Z  * [new tag]                 ciflow/slow/0577043         -> ciflow/slow/0577043
2025-03-04T20:02:07.3132081Z  * [new tag]                 ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym
2025-03-04T20:02:07.3132346Z  * [new tag]                 ciflow/slow/0e81104         -> ciflow/slow/0e81104
2025-03-04T20:02:07.3132974Z  * [new tag]                 ciflow/slow/139975          -> ciflow/slow/139975
2025-03-04T20:02:07.3133599Z  * [new tag]                 ciflow/slow/146256          -> ciflow/slow/146256
2025-03-04T20:02:07.3134214Z  * [new tag]                 ciflow/slow/146903          -> ciflow/slow/146903
2025-03-04T20:02:07.3135114Z  * [new tag]                 ciflow/slow/1732077         -> ciflow/slow/1732077
2025-03-04T20:02:07.3136282Z  * [new tag]                 ciflow/slow/187eb7c         -> ciflow/slow/187eb7c
2025-03-04T20:02:07.3137335Z  * [new tag]                 ciflow/slow/1faef89         -> ciflow/slow/1faef89
2025-03-04T20:02:07.3138508Z  * [new tag]                 ciflow/slow/3920ec1         -> ciflow/slow/3920ec1
2025-03-04T20:02:07.3139212Z  * [new tag]                 ciflow/slow/3b7c6b2         -> ciflow/slow/3b7c6b2
2025-03-04T20:02:07.3140203Z  * [new tag]                 ciflow/slow/59a3759         -> ciflow/slow/59a3759
2025-03-04T20:02:07.3140945Z  * [new tag]                 ciflow/slow/70ef0bb         -> ciflow/slow/70ef0bb
2025-03-04T20:02:07.3141779Z  * [new tag]                 ciflow/slow/788ff06         -> ciflow/slow/788ff06
2025-03-04T20:02:07.3143105Z  * [new tag]                 ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym
2025-03-04T20:02:07.3143497Z  * [new tag]                 ciflow/slow/9d85864         -> ciflow/slow/9d85864
2025-03-04T20:02:07.3144352Z  * [new tag]                 ciflow/slow/9ffad5b         -> ciflow/slow/9ffad5b
2025-03-04T20:02:07.3145076Z  * [new tag]                 ciflow/slow/a206e8b         -> ciflow/slow/a206e8b
2025-03-04T20:02:07.3145984Z  * [new tag]                 ciflow/slow/a837609         -> ciflow/slow/a837609
2025-03-04T20:02:07.3146819Z  * [new tag]                 ciflow/slow/af841f3         -> ciflow/slow/af841f3
2025-03-04T20:02:07.3148082Z  * [new tag]                 ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym
2025-03-04T20:02:07.3148509Z  * [new tag]                 ciflow/trunk/108303         -> ciflow/trunk/108303
2025-03-04T20:02:07.3149152Z  * [new tag]                 ciflow/trunk/113257         -> ciflow/trunk/113257
2025-03-04T20:02:07.3150284Z  * [new tag]                 ciflow/trunk/113258         -> ciflow/trunk/113258
2025-03-04T20:02:07.3150799Z  * [new tag]                 ciflow/trunk/120076         -> ciflow/trunk/120076
2025-03-04T20:02:07.3151501Z  * [new tag]                 ciflow/trunk/121445         -> ciflow/trunk/121445
2025-03-04T20:02:07.3152079Z  * [new tag]                 ciflow/trunk/123020         -> ciflow/trunk/123020
2025-03-04T20:02:07.3152750Z  * [new tag]                 ciflow/trunk/124424         -> ciflow/trunk/124424
2025-03-04T20:02:07.3153327Z  * [new tag]                 ciflow/trunk/124490         -> ciflow/trunk/124490
2025-03-04T20:02:07.3153967Z  * [new tag]                 ciflow/trunk/125469         -> ciflow/trunk/125469
2025-03-04T20:02:07.3154559Z  * [new tag]                 ciflow/trunk/125806         -> ciflow/trunk/125806
2025-03-04T20:02:07.3155183Z  * [new tag]                 ciflow/trunk/125888         -> ciflow/trunk/125888
2025-03-04T20:02:07.3156138Z  * [new tag]                 ciflow/trunk/125995         -> ciflow/trunk/125995
2025-03-04T20:02:07.3156884Z  * [new tag]                 ciflow/trunk/126050         -> ciflow/trunk/126050
2025-03-04T20:02:07.3157756Z  * [new tag]                 ciflow/trunk/126054         -> ciflow/trunk/126054
2025-03-04T20:02:07.3158631Z  * [new tag]                 ciflow/trunk/126635         -> ciflow/trunk/126635
2025-03-04T20:02:07.3159424Z  * [new tag]                 ciflow/trunk/127171         -> ciflow/trunk/127171
2025-03-04T20:02:07.3160098Z  * [new tag]                 ciflow/trunk/127919         -> ciflow/trunk/127919
2025-03-04T20:02:07.3160774Z  * [new tag]                 ciflow/trunk/129352         -> ciflow/trunk/129352
2025-03-04T20:02:07.3161412Z  * [new tag]                 ciflow/trunk/129420         -> ciflow/trunk/129420
2025-03-04T20:02:07.3162089Z  * [new tag]                 ciflow/trunk/130141         -> ciflow/trunk/130141
2025-03-04T20:02:07.3162717Z  * [new tag]                 ciflow/trunk/130752         -> ciflow/trunk/130752
2025-03-04T20:02:07.3163591Z  * [new tag]                 ciflow/trunk/131354         -> ciflow/trunk/131354
2025-03-04T20:02:07.3164572Z  * [new tag]                 ciflow/trunk/131507         -> ciflow/trunk/131507
2025-03-04T20:02:07.3165121Z  * [new tag]                 ciflow/trunk/132021         -> ciflow/trunk/132021
2025-03-04T20:02:07.3165771Z  * [new tag]                 ciflow/trunk/133044         -> ciflow/trunk/133044
2025-03-04T20:02:07.3166403Z  * [new tag]                 ciflow/trunk/133289         -> ciflow/trunk/133289
2025-03-04T20:02:07.3167057Z  * [new tag]                 ciflow/trunk/133296         -> ciflow/trunk/133296
2025-03-04T20:02:07.3167729Z  * [new tag]                 ciflow/trunk/133297         -> ciflow/trunk/133297
2025-03-04T20:02:07.3168383Z  * [new tag]                 ciflow/trunk/133315         -> ciflow/trunk/133315
2025-03-04T20:02:07.3169073Z  * [new tag]                 ciflow/trunk/133392         -> ciflow/trunk/133392
2025-03-04T20:02:07.3169724Z  * [new tag]                 ciflow/trunk/133419         -> ciflow/trunk/133419
2025-03-04T20:02:07.3170551Z  * [new tag]                 ciflow/trunk/133423         -> ciflow/trunk/133423
2025-03-04T20:02:07.3171276Z  * [new tag]                 ciflow/trunk/133667         -> ciflow/trunk/133667
2025-03-04T20:02:07.3171737Z  * [new tag]                 ciflow/trunk/133753         -> ciflow/trunk/133753
2025-03-04T20:02:07.3172618Z  * [new tag]                 ciflow/trunk/134219         -> ciflow/trunk/134219
2025-03-04T20:02:07.3173299Z  * [new tag]                 ciflow/trunk/134515         -> ciflow/trunk/134515
2025-03-04T20:02:07.3174151Z  * [new tag]                 ciflow/trunk/135058         -> ciflow/trunk/135058
2025-03-04T20:02:07.3175204Z  * [new tag]                 ciflow/trunk/135631         -> ciflow/trunk/135631
2025-03-04T20:02:07.3175901Z  * [new tag]                 ciflow/trunk/136780         -> ciflow/trunk/136780
2025-03-04T20:02:07.3176816Z  * [new tag]                 ciflow/trunk/136824         -> ciflow/trunk/136824
2025-03-04T20:02:07.3177451Z  * [new tag]                 ciflow/trunk/136835         -> ciflow/trunk/136835
2025-03-04T20:02:07.3178472Z  * [new tag]                 ciflow/trunk/136993         -> ciflow/trunk/136993
2025-03-04T20:02:07.3179076Z  * [new tag]                 ciflow/trunk/137400         -> ciflow/trunk/137400
2025-03-04T20:02:07.3179772Z  * [new tag]                 ciflow/trunk/137580         -> ciflow/trunk/137580
2025-03-04T20:02:07.3180389Z  * [new tag]                 ciflow/trunk/138213         -> ciflow/trunk/138213
2025-03-04T20:02:07.3181026Z  * [new tag]                 ciflow/trunk/138436         -> ciflow/trunk/138436
2025-03-04T20:02:07.3181678Z  * [new tag]                 ciflow/trunk/138626         -> ciflow/trunk/138626
2025-03-04T20:02:07.3182347Z  * [new tag]                 ciflow/trunk/138834         -> ciflow/trunk/138834
2025-03-04T20:02:07.3183051Z  * [new tag]                 ciflow/trunk/138889         -> ciflow/trunk/138889
2025-03-04T20:02:07.3183670Z  * [new tag]                 ciflow/trunk/138996         -> ciflow/trunk/138996
2025-03-04T20:02:07.3184417Z  * [new tag]                 ciflow/trunk/139070         -> ciflow/trunk/139070
2025-03-04T20:02:07.3185121Z  * [new tag]                 ciflow/trunk/139094         -> ciflow/trunk/139094
2025-03-04T20:02:07.3185873Z  * [new tag]                 ciflow/trunk/139971         -> ciflow/trunk/139971
2025-03-04T20:02:07.3186549Z  * [new tag]                 ciflow/trunk/139975         -> ciflow/trunk/139975
2025-03-04T20:02:07.3187168Z  * [new tag]                 ciflow/trunk/140084         -> ciflow/trunk/140084
2025-03-04T20:02:07.3187906Z  * [new tag]                 ciflow/trunk/140159         -> ciflow/trunk/140159
2025-03-04T20:02:07.3188539Z  * [new tag]                 ciflow/trunk/140211         -> ciflow/trunk/140211
2025-03-04T20:02:07.3189131Z  * [new tag]                 ciflow/trunk/140298         -> ciflow/trunk/140298
2025-03-04T20:02:07.3189767Z  * [new tag]                 ciflow/trunk/140323         -> ciflow/trunk/140323
2025-03-04T20:02:07.3190443Z  * [new tag]                 ciflow/trunk/140365         -> ciflow/trunk/140365
2025-03-04T20:02:07.3191296Z  * [new tag]                 ciflow/trunk/140399         -> ciflow/trunk/140399
2025-03-04T20:02:07.3191903Z  * [new tag]                 ciflow/trunk/140793         -> ciflow/trunk/140793
2025-03-04T20:02:07.3192588Z  * [new tag]                 ciflow/trunk/140979         -> ciflow/trunk/140979
2025-03-04T20:02:07.3193199Z  * [new tag]                 ciflow/trunk/140989         -> ciflow/trunk/140989
2025-03-04T20:02:07.3193859Z  * [new tag]                 ciflow/trunk/141178         -> ciflow/trunk/141178
2025-03-04T20:02:07.3194741Z  * [new tag]                 ciflow/trunk/141257         -> ciflow/trunk/141257
2025-03-04T20:02:07.3195855Z  * [new tag]                 ciflow/trunk/141309         -> ciflow/trunk/141309
2025-03-04T20:02:07.3196470Z  * [new tag]                 ciflow/trunk/141730         -> ciflow/trunk/141730
2025-03-04T20:02:07.3197372Z  * [new tag]                 ciflow/trunk/141796         -> ciflow/trunk/141796
2025-03-04T20:02:07.3197915Z  * [new tag]                 ciflow/trunk/141842         -> ciflow/trunk/141842
2025-03-04T20:02:07.3198573Z  * [new tag]                 ciflow/trunk/141889         -> ciflow/trunk/141889
2025-03-04T20:02:07.3199267Z  * [new tag]                 ciflow/trunk/141910         -> ciflow/trunk/141910
2025-03-04T20:02:07.3200181Z  * [new tag]                 ciflow/trunk/141914         -> ciflow/trunk/141914
2025-03-04T20:02:07.3200727Z  * [new tag]                 ciflow/trunk/141961         -> ciflow/trunk/141961
2025-03-04T20:02:07.3201401Z  * [new tag]                 ciflow/trunk/142091         -> ciflow/trunk/142091
2025-03-04T20:02:07.3202063Z  * [new tag]                 ciflow/trunk/142092         -> ciflow/trunk/142092
2025-03-04T20:02:07.3202681Z  * [new tag]                 ciflow/trunk/142097         -> ciflow/trunk/142097
2025-03-04T20:02:07.3203336Z  * [new tag]                 ciflow/trunk/142179         -> ciflow/trunk/142179
2025-03-04T20:02:07.3203993Z  * [new tag]                 ciflow/trunk/142272         -> ciflow/trunk/142272
2025-03-04T20:02:07.3204630Z  * [new tag]                 ciflow/trunk/142273         -> ciflow/trunk/142273
2025-03-04T20:02:07.3205483Z  * [new tag]                 ciflow/trunk/142326         -> ciflow/trunk/142326
2025-03-04T20:02:07.3206109Z  * [new tag]                 ciflow/trunk/142346         -> ciflow/trunk/142346
2025-03-04T20:02:07.3206774Z  * [new tag]                 ciflow/trunk/142350         -> ciflow/trunk/142350
2025-03-04T20:02:07.3207414Z  * [new tag]                 ciflow/trunk/142372         -> ciflow/trunk/142372
2025-03-04T20:02:07.3208106Z  * [new tag]                 ciflow/trunk/142477         -> ciflow/trunk/142477
2025-03-04T20:02:07.3209133Z  * [new tag]                 ciflow/trunk/142821         -> ciflow/trunk/142821
2025-03-04T20:02:07.3209683Z  * [new tag]                 ciflow/trunk/142859         -> ciflow/trunk/142859
2025-03-04T20:02:07.3210555Z  * [new tag]                 ciflow/trunk/142865         -> ciflow/trunk/142865
2025-03-04T20:02:07.3211151Z  * [new tag]                 ciflow/trunk/143082         -> ciflow/trunk/143082
2025-03-04T20:02:07.3212138Z  * [new tag]                 ciflow/trunk/143093         -> ciflow/trunk/143093
2025-03-04T20:02:07.3212712Z  * [new tag]                 ciflow/trunk/143220         -> ciflow/trunk/143220
2025-03-04T20:02:07.3213472Z  * [new tag]                 ciflow/trunk/143261         -> ciflow/trunk/143261
2025-03-04T20:02:07.3214128Z  * [new tag]                 ciflow/trunk/143303         -> ciflow/trunk/143303
2025-03-04T20:02:07.3214803Z  * [new tag]                 ciflow/trunk/143313         -> ciflow/trunk/143313
2025-03-04T20:02:07.3215687Z  * [new tag]                 ciflow/trunk/143347         -> ciflow/trunk/143347
2025-03-04T20:02:07.3216436Z  * [new tag]                 ciflow/trunk/143402         -> ciflow/trunk/143402
2025-03-04T20:02:07.3217067Z  * [new tag]                 ciflow/trunk/143416         -> ciflow/trunk/143416
2025-03-04T20:02:07.3218020Z  * [new tag]                 ciflow/trunk/143451         -> ciflow/trunk/143451
2025-03-04T20:02:07.3218630Z  * [new tag]                 ciflow/trunk/143475         -> ciflow/trunk/143475
2025-03-04T20:02:07.3219271Z  * [new tag]                 ciflow/trunk/143630         -> ciflow/trunk/143630
2025-03-04T20:02:07.3219970Z  * [new tag]                 ciflow/trunk/143666         -> ciflow/trunk/143666
2025-03-04T20:02:07.3220609Z  * [new tag]                 ciflow/trunk/143671         -> ciflow/trunk/143671
2025-03-04T20:02:07.3221553Z  * [new tag]                 ciflow/trunk/143689         -> ciflow/trunk/143689
2025-03-04T20:02:07.3222142Z  * [new tag]                 ciflow/trunk/143712         -> ciflow/trunk/143712
2025-03-04T20:02:07.3223009Z  * [new tag]                 ciflow/trunk/143733         -> ciflow/trunk/143733
2025-03-04T20:02:07.3223685Z  * [new tag]                 ciflow/trunk/143822         -> ciflow/trunk/143822
2025-03-04T20:02:07.3224608Z  * [new tag]                 ciflow/trunk/143833         -> ciflow/trunk/143833
2025-03-04T20:02:07.3225482Z  * [new tag]                 ciflow/trunk/143894         -> ciflow/trunk/143894
2025-03-04T20:02:07.3226191Z  * [new tag]                 ciflow/trunk/143896         -> ciflow/trunk/143896
2025-03-04T20:02:07.3226750Z  * [new tag]                 ciflow/trunk/143961         -> ciflow/trunk/143961
2025-03-04T20:02:07.3227402Z  * [new tag]                 ciflow/trunk/143966         -> ciflow/trunk/143966
2025-03-04T20:02:07.3228026Z  * [new tag]                 ciflow/trunk/144017         -> ciflow/trunk/144017
2025-03-04T20:02:07.3228879Z  * [new tag]                 ciflow/trunk/144019         -> ciflow/trunk/144019
2025-03-04T20:02:07.3229473Z  * [new tag]                 ciflow/trunk/144120         -> ciflow/trunk/144120
2025-03-04T20:02:07.3230400Z  * [new tag]                 ciflow/trunk/144138         -> ciflow/trunk/144138
2025-03-04T20:02:07.3230897Z  * [new tag]                 ciflow/trunk/144172         -> ciflow/trunk/144172
2025-03-04T20:02:07.3231767Z  * [new tag]                 ciflow/trunk/144177         -> ciflow/trunk/144177
2025-03-04T20:02:07.3232706Z  * [new tag]                 ciflow/trunk/144268         -> ciflow/trunk/144268
2025-03-04T20:02:07.3233302Z  * [new tag]                 ciflow/trunk/144272         -> ciflow/trunk/144272
2025-03-04T20:02:07.3233939Z  * [new tag]                 ciflow/trunk/144293         -> ciflow/trunk/144293
2025-03-04T20:02:07.3234622Z  * [new tag]                 ciflow/trunk/144452         -> ciflow/trunk/144452
2025-03-04T20:02:07.3235462Z  * [new tag]                 ciflow/trunk/144468         -> ciflow/trunk/144468
2025-03-04T20:02:07.3236058Z  * [new tag]                 ciflow/trunk/144557         -> ciflow/trunk/144557
2025-03-04T20:02:07.3236706Z  * [new tag]                 ciflow/trunk/144572         -> ciflow/trunk/144572
2025-03-04T20:02:07.3237677Z  * [new tag]                 ciflow/trunk/144590         -> ciflow/trunk/144590
2025-03-04T20:02:07.3238537Z  * [new tag]                 ciflow/trunk/144616         -> ciflow/trunk/144616
2025-03-04T20:02:07.3239112Z  * [new tag]                 ciflow/trunk/144620         -> ciflow/trunk/144620
2025-03-04T20:02:07.3239861Z  * [new tag]                 ciflow/trunk/144664         -> ciflow/trunk/144664
2025-03-04T20:02:07.3240572Z  * [new tag]                 ciflow/trunk/144708         -> ciflow/trunk/144708
2025-03-04T20:02:07.3241233Z  * [new tag]                 ciflow/trunk/144721         -> ciflow/trunk/144721
2025-03-04T20:02:07.3242368Z  * [new tag]                 ciflow/trunk/144733         -> ciflow/trunk/144733
2025-03-04T20:02:07.3243245Z  * [new tag]                 ciflow/trunk/144763         -> ciflow/trunk/144763
2025-03-04T20:02:07.3243837Z  * [new tag]                 ciflow/trunk/144771         -> ciflow/trunk/144771
2025-03-04T20:02:07.3244535Z  * [new tag]                 ciflow/trunk/144844         -> ciflow/trunk/144844
2025-03-04T20:02:07.3245219Z  * [new tag]                 ciflow/trunk/144880         -> ciflow/trunk/144880
2025-03-04T20:02:07.3245872Z  * [new tag]                 ciflow/trunk/144925         -> ciflow/trunk/144925
2025-03-04T20:02:07.3246541Z  * [new tag]                 ciflow/trunk/144953         -> ciflow/trunk/144953
2025-03-04T20:02:07.3247191Z  * [new tag]                 ciflow/trunk/144975         -> ciflow/trunk/144975
2025-03-04T20:02:07.3247874Z  * [new tag]                 ciflow/trunk/144992         -> ciflow/trunk/144992
2025-03-04T20:02:07.3248596Z  * [new tag]                 ciflow/trunk/145061         -> ciflow/trunk/145061
2025-03-04T20:02:07.3249264Z  * [new tag]                 ciflow/trunk/145116         -> ciflow/trunk/145116
2025-03-04T20:02:07.3249894Z  * [new tag]                 ciflow/trunk/145119         -> ciflow/trunk/145119
2025-03-04T20:02:07.3250805Z  * [new tag]                 ciflow/trunk/145136         -> ciflow/trunk/145136
2025-03-04T20:02:07.3251302Z  * [new tag]                 ciflow/trunk/145153         -> ciflow/trunk/145153
2025-03-04T20:02:07.3251974Z  * [new tag]                 ciflow/trunk/145224         -> ciflow/trunk/145224
2025-03-04T20:02:07.3252638Z  * [new tag]                 ciflow/trunk/145241         -> ciflow/trunk/145241
2025-03-04T20:02:07.3253312Z  * [new tag]                 ciflow/trunk/145254         -> ciflow/trunk/145254
2025-03-04T20:02:07.3253948Z  * [new tag]                 ciflow/trunk/145331         -> ciflow/trunk/145331
2025-03-04T20:02:07.3255216Z  * [new tag]                 ciflow/trunk/145406         -> ciflow/trunk/145406
2025-03-04T20:02:07.3256055Z  * [new tag]                 ciflow/trunk/145523         -> ciflow/trunk/145523
2025-03-04T20:02:07.3256672Z  * [new tag]                 ciflow/trunk/145559         -> ciflow/trunk/145559
2025-03-04T20:02:07.3257518Z  * [new tag]                 ciflow/trunk/145677         -> ciflow/trunk/145677
2025-03-04T20:02:07.3258165Z  * [new tag]                 ciflow/trunk/145717         -> ciflow/trunk/145717
2025-03-04T20:02:07.3258853Z  * [new tag]                 ciflow/trunk/145936         -> ciflow/trunk/145936
2025-03-04T20:02:07.3259523Z  * [new tag]                 ciflow/trunk/145946         -> ciflow/trunk/145946
2025-03-04T20:02:07.3260131Z  * [new tag]                 ciflow/trunk/145966         -> ciflow/trunk/145966
2025-03-04T20:02:07.3260854Z  * [new tag]                 ciflow/trunk/145979         -> ciflow/trunk/145979
2025-03-04T20:02:07.3261454Z  * [new tag]                 ciflow/trunk/146051         -> ciflow/trunk/146051
2025-03-04T20:02:07.3262444Z  * [new tag]                 ciflow/trunk/146069         -> ciflow/trunk/146069
2025-03-04T20:02:07.3263076Z  * [new tag]                 ciflow/trunk/146090         -> ciflow/trunk/146090
2025-03-04T20:02:07.3263741Z  * [new tag]                 ciflow/trunk/146098         -> ciflow/trunk/146098
2025-03-04T20:02:07.3264514Z  * [new tag]                 ciflow/trunk/146110         -> ciflow/trunk/146110
2025-03-04T20:02:07.3265200Z  * [new tag]                 ciflow/trunk/146115         -> ciflow/trunk/146115
2025-03-04T20:02:07.3266082Z  * [new tag]                 ciflow/trunk/146176         -> ciflow/trunk/146176
2025-03-04T20:02:07.3267118Z  * [new tag]                 ciflow/trunk/146182         -> ciflow/trunk/146182
2025-03-04T20:02:07.3267719Z  * [new tag]                 ciflow/trunk/146256         -> ciflow/trunk/146256
2025-03-04T20:02:07.3268351Z  * [new tag]                 ciflow/trunk/146275         -> ciflow/trunk/146275
2025-03-04T20:02:07.3269203Z  * [new tag]                 ciflow/trunk/146289         -> ciflow/trunk/146289
2025-03-04T20:02:07.3269783Z  * [new tag]                 ciflow/trunk/146335         -> ciflow/trunk/146335
2025-03-04T20:02:07.3270454Z  * [new tag]                 ciflow/trunk/146421         -> ciflow/trunk/146421
2025-03-04T20:02:07.3271412Z  * [new tag]                 ciflow/trunk/146489         -> ciflow/trunk/146489
2025-03-04T20:02:07.3272086Z  * [new tag]                 ciflow/trunk/146517         -> ciflow/trunk/146517
2025-03-04T20:02:07.3272728Z  * [new tag]                 ciflow/trunk/146530         -> ciflow/trunk/146530
2025-03-04T20:02:07.3273396Z  * [new tag]                 ciflow/trunk/146561         -> ciflow/trunk/146561
2025-03-04T20:02:07.3274258Z  * [new tag]                 ciflow/trunk/146573         -> ciflow/trunk/146573
2025-03-04T20:02:07.3275118Z  * [new tag]                 ciflow/trunk/146582         -> ciflow/trunk/146582
2025-03-04T20:02:07.3275768Z  * [new tag]                 ciflow/trunk/146661         -> ciflow/trunk/146661
2025-03-04T20:02:07.3276425Z  * [new tag]                 ciflow/trunk/146718         -> ciflow/trunk/146718
2025-03-04T20:02:07.3277177Z  * [new tag]                 ciflow/trunk/146777         -> ciflow/trunk/146777
2025-03-04T20:02:07.3278051Z  * [new tag]                 ciflow/trunk/146807         -> ciflow/trunk/146807
2025-03-04T20:02:07.3278691Z  * [new tag]                 ciflow/trunk/146823         -> ciflow/trunk/146823
2025-03-04T20:02:07.3279416Z  * [new tag]                 ciflow/trunk/146826         -> ciflow/trunk/146826
2025-03-04T20:02:07.3280104Z  * [new tag]                 ciflow/trunk/146827         -> ciflow/trunk/146827
2025-03-04T20:02:07.3280750Z  * [new tag]                 ciflow/trunk/146845         -> ciflow/trunk/146845
2025-03-04T20:02:07.3281441Z  * [new tag]                 ciflow/trunk/146870         -> ciflow/trunk/146870
2025-03-04T20:02:07.3282070Z  * [new tag]                 ciflow/trunk/146873         -> ciflow/trunk/146873
2025-03-04T20:02:07.3282715Z  * [new tag]                 ciflow/trunk/146874         -> ciflow/trunk/146874
2025-03-04T20:02:07.3283385Z  * [new tag]                 ciflow/trunk/146903         -> ciflow/trunk/146903
2025-03-04T20:02:07.3284050Z  * [new tag]                 ciflow/trunk/146928         -> ciflow/trunk/146928
2025-03-04T20:02:07.3285018Z  * [new tag]                 ciflow/trunk/146970         -> ciflow/trunk/146970
2025-03-04T20:02:07.3285616Z  * [new tag]                 ciflow/trunk/147014         -> ciflow/trunk/147014
2025-03-04T20:02:07.3286255Z  * [new tag]                 ciflow/trunk/147072         -> ciflow/trunk/147072
2025-03-04T20:02:07.3286891Z  * [new tag]                 ciflow/trunk/147105         -> ciflow/trunk/147105
2025-03-04T20:02:07.3287565Z  * [new tag]                 ciflow/trunk/147155         -> ciflow/trunk/147155
2025-03-04T20:02:07.3288693Z  * [new tag]                 ciflow/trunk/147243         -> ciflow/trunk/147243
2025-03-04T20:02:07.3289360Z  * [new tag]                 ciflow/trunk/147272         -> ciflow/trunk/147272
2025-03-04T20:02:07.3289948Z  * [new tag]                 ciflow/trunk/147314         -> ciflow/trunk/147314
2025-03-04T20:02:07.3290556Z  * [new tag]                 ciflow/trunk/147320         -> ciflow/trunk/147320
2025-03-04T20:02:07.3291474Z  * [new tag]                 ciflow/trunk/147334         -> ciflow/trunk/147334
2025-03-04T20:02:07.3292073Z  * [new tag]                 ciflow/trunk/147349         -> ciflow/trunk/147349
2025-03-04T20:02:07.3292713Z  * [new tag]                 ciflow/trunk/147368         -> ciflow/trunk/147368
2025-03-04T20:02:07.3293388Z  * [new tag]                 ciflow/trunk/147403         -> ciflow/trunk/147403
2025-03-04T20:02:07.3294033Z  * [new tag]                 ciflow/trunk/147422         -> ciflow/trunk/147422
2025-03-04T20:02:07.3294834Z  * [new tag]                 ciflow/trunk/147448         -> ciflow/trunk/147448
2025-03-04T20:02:07.3295368Z  * [new tag]                 ciflow/trunk/147452         -> ciflow/trunk/147452
2025-03-04T20:02:07.3296046Z  * [new tag]                 ciflow/trunk/147481         -> ciflow/trunk/147481
2025-03-04T20:02:07.3296686Z  * [new tag]                 ciflow/trunk/147498         -> ciflow/trunk/147498
2025-03-04T20:02:07.3297359Z  * [new tag]                 ciflow/trunk/147574         -> ciflow/trunk/147574
2025-03-04T20:02:07.3298058Z  * [new tag]                 ciflow/trunk/147583         -> ciflow/trunk/147583
2025-03-04T20:02:07.3298751Z  * [new tag]                 ciflow/trunk/147660         -> ciflow/trunk/147660
2025-03-04T20:02:07.3299428Z  * [new tag]                 ciflow/trunk/147664         -> ciflow/trunk/147664
2025-03-04T20:02:07.3300105Z  * [new tag]                 ciflow/trunk/147741         -> ciflow/trunk/147741
2025-03-04T20:02:07.3300967Z  * [new tag]                 ciflow/trunk/147742         -> ciflow/trunk/147742
2025-03-04T20:02:07.3301645Z  * [new tag]                 ciflow/trunk/147752         -> ciflow/trunk/147752
2025-03-04T20:02:07.3302349Z  * [new tag]                 ciflow/trunk/147797         -> ciflow/trunk/147797
2025-03-04T20:02:07.3302976Z  * [new tag]                 ciflow/trunk/147798         -> ciflow/trunk/147798
2025-03-04T20:02:07.3303874Z  * [new tag]                 ciflow/trunk/147808         -> ciflow/trunk/147808
2025-03-04T20:02:07.3304486Z  * [new tag]                 ciflow/trunk/147817         -> ciflow/trunk/147817
2025-03-04T20:02:07.3305664Z  * [new tag]                 ciflow/trunk/147820         -> ciflow/trunk/147820
2025-03-04T20:02:07.3306229Z  * [new tag]                 ciflow/trunk/147821         -> ciflow/trunk/147821
2025-03-04T20:02:07.3306908Z  * [new tag]                 ciflow/trunk/147836         -> ciflow/trunk/147836
2025-03-04T20:02:07.3307638Z  * [new tag]                 ciflow/trunk/147862         -> ciflow/trunk/147862
2025-03-04T20:02:07.3308241Z  * [new tag]                 ciflow/trunk/147870         -> ciflow/trunk/147870
2025-03-04T20:02:07.3308975Z  * [new tag]                 ciflow/trunk/147881         -> ciflow/trunk/147881
2025-03-04T20:02:07.3309623Z  * [new tag]                 ciflow/trunk/147897         -> ciflow/trunk/147897
2025-03-04T20:02:07.3310323Z  * [new tag]                 ciflow/trunk/147910         -> ciflow/trunk/147910
2025-03-04T20:02:07.3310947Z  * [new tag]                 ciflow/trunk/147917         -> ciflow/trunk/147917
2025-03-04T20:02:07.3311684Z  * [new tag]                 ciflow/trunk/147945         -> ciflow/trunk/147945
2025-03-04T20:02:07.3312354Z  * [new tag]                 ciflow/trunk/147955         -> ciflow/trunk/147955
2025-03-04T20:02:07.3313026Z  * [new tag]                 ciflow/trunk/147956         -> ciflow/trunk/147956
2025-03-04T20:02:07.3313648Z  * [new tag]                 ciflow/trunk/147957         -> ciflow/trunk/147957
2025-03-04T20:02:07.3314316Z  * [new tag]                 ciflow/trunk/147958         -> ciflow/trunk/147958
2025-03-04T20:02:07.3315009Z  * [new tag]                 ciflow/trunk/147959         -> ciflow/trunk/147959
2025-03-04T20:02:07.3315706Z  * [new tag]                 ciflow/trunk/147962         -> ciflow/trunk/147962
2025-03-04T20:02:07.3316335Z  * [new tag]                 ciflow/trunk/147964         -> ciflow/trunk/147964
2025-03-04T20:02:07.3317023Z  * [new tag]                 ciflow/trunk/147994         -> ciflow/trunk/147994
2025-03-04T20:02:07.3317653Z  * [new tag]                 ciflow/trunk/147997         -> ciflow/trunk/147997
2025-03-04T20:02:07.3318532Z  * [new tag]                 ciflow/trunk/148049         -> ciflow/trunk/148049
2025-03-04T20:02:07.3319044Z  * [new tag]                 ciflow/trunk/148076         -> ciflow/trunk/148076
2025-03-04T20:02:07.3319712Z  * [new tag]                 ciflow/trunk/148083         -> ciflow/trunk/148083
2025-03-04T20:02:07.3320499Z  * [new tag]                 ciflow/trunk/148131         -> ciflow/trunk/148131
2025-03-04T20:02:07.3321089Z  * [new tag]                 ciflow/trunk/148163         -> ciflow/trunk/148163
2025-03-04T20:02:07.3321725Z  * [new tag]                 ciflow/trunk/148173         -> ciflow/trunk/148173
2025-03-04T20:02:07.3322402Z  * [new tag]                 ciflow/trunk/148180         -> ciflow/trunk/148180
2025-03-04T20:02:07.3323054Z  * [new tag]                 ciflow/trunk/148231         -> ciflow/trunk/148231
2025-03-04T20:02:07.3323958Z  * [new tag]                 ciflow/trunk/148261         -> ciflow/trunk/148261
2025-03-04T20:02:07.3324597Z  * [new tag]                 ciflow/trunk/148266         -> ciflow/trunk/148266
2025-03-04T20:02:07.3325195Z  * [new tag]                 ciflow/trunk/148279         -> ciflow/trunk/148279
2025-03-04T20:02:07.3325856Z  * [new tag]                 ciflow/trunk/148290         -> ciflow/trunk/148290
2025-03-04T20:02:07.3326530Z  * [new tag]                 ciflow/trunk/148292         -> ciflow/trunk/148292
2025-03-04T20:02:07.3327134Z  * [new tag]                 ciflow/trunk/148305         -> ciflow/trunk/148305
2025-03-04T20:02:07.3327811Z  * [new tag]                 ciflow/trunk/148343         -> ciflow/trunk/148343
2025-03-04T20:02:07.3328479Z  * [new tag]                 ciflow/trunk/148350         -> ciflow/trunk/148350
2025-03-04T20:02:07.3329185Z  * [new tag]                 ciflow/trunk/148364         -> ciflow/trunk/148364
2025-03-04T20:02:07.3329800Z  * [new tag]                 ciflow/trunk/148366         -> ciflow/trunk/148366
2025-03-04T20:02:07.3330498Z  * [new tag]                 ciflow/trunk/148371         -> ciflow/trunk/148371
2025-03-04T20:02:07.3331140Z  * [new tag]                 ciflow/trunk/148388         -> ciflow/trunk/148388
2025-03-04T20:02:07.3331894Z  * [new tag]                 ciflow/trunk/148423         -> ciflow/trunk/148423
2025-03-04T20:02:07.3333097Z  * [new tag]                 ciflow/trunk/70978          -> ciflow/trunk/70978
2025-03-04T20:02:07.3333874Z  * [new tag]                 ciflow/trunk/70979          -> ciflow/trunk/70979
2025-03-04T20:02:07.3335022Z  * [new tag]                 ciflow/unstable/123         -> ciflow/unstable/123
2025-03-04T20:02:07.3335667Z  * [new tag]                 ciflow/unstable/146104      -> ciflow/unstable/146104
2025-03-04T20:02:07.3336411Z  * [new tag]                 ciflow/unstable/146264      -> ciflow/unstable/146264
2025-03-04T20:02:07.3337056Z  * [new tag]                 ciflow/unstable/147320      -> ciflow/unstable/147320
2025-03-04T20:02:07.3337989Z  * [new tag]                 ciflow/xpu/137566           -> ciflow/xpu/137566
2025-03-04T20:02:07.3338621Z  * [new tag]                 ciflow/xpu/137580           -> ciflow/xpu/137580
2025-03-04T20:02:07.3339228Z  * [new tag]                 ciflow/xpu/138889           -> ciflow/xpu/138889
2025-03-04T20:02:07.3339847Z  * [new tag]                 ciflow/xpu/138996           -> ciflow/xpu/138996
2025-03-04T20:02:07.3340524Z  * [new tag]                 ciflow/xpu/139469           -> ciflow/xpu/139469
2025-03-04T20:02:07.3341013Z  * [new tag]                 ciflow/xpu/139971           -> ciflow/xpu/139971
2025-03-04T20:02:07.3341629Z  * [new tag]                 ciflow/xpu/140365           -> ciflow/xpu/140365
2025-03-04T20:02:07.3342347Z  * [new tag]                 ciflow/xpu/140372           -> ciflow/xpu/140372
2025-03-04T20:02:07.3342917Z  * [new tag]                 ciflow/xpu/140686           -> ciflow/xpu/140686
2025-03-04T20:02:07.3343705Z  * [new tag]                 ciflow/xpu/140972           -> ciflow/xpu/140972
2025-03-04T20:02:07.3346486Z  * [new tag]                 ciflow/xpu/142040           -> ciflow/xpu/142040
2025-03-04T20:02:07.3346690Z  * [new tag]                 ciflow/xpu/142097           -> ciflow/xpu/142097
2025-03-04T20:02:07.3347394Z  * [new tag]                 ciflow/xpu/143597           -> ciflow/xpu/143597
2025-03-04T20:02:07.3347608Z  * [new tag]                 ciflow/xpu/143833           -> ciflow/xpu/143833
2025-03-04T20:02:07.3349056Z  * [new tag]                 ciflow/xpu/144240           -> ciflow/xpu/144240
2025-03-04T20:02:07.3349971Z  * [new tag]                 ciflow/xpu/144452           -> ciflow/xpu/144452
2025-03-04T20:02:07.3350715Z  * [new tag]                 ciflow/xpu/144664           -> ciflow/xpu/144664
2025-03-04T20:02:07.3351679Z  * [new tag]                 ciflow/xpu/146098           -> ciflow/xpu/146098
2025-03-04T20:02:07.3352970Z  * [new tag]                 ciflow/xpu/147161           -> ciflow/xpu/147161
2025-03-04T20:02:07.3353850Z  * [new tag]                 ciflow/xpu/147349           -> ciflow/xpu/147349
2025-03-04T20:02:07.3355200Z  * [new tag]                 ciflow/xpu/147355           -> ciflow/xpu/147355
2025-03-04T20:02:07.3356106Z  * [new tag]                 ciflow/xpu/147403           -> ciflow/xpu/147403
2025-03-04T20:02:07.3357117Z  * [new tag]                 ciflow/xpu/147448           -> ciflow/xpu/147448
2025-03-04T20:02:07.3358100Z  * [new tag]                 ciflow/xpu/147498           -> ciflow/xpu/147498
2025-03-04T20:02:07.3359120Z  * [new tag]                 ciflow/xpu/147507           -> ciflow/xpu/147507
2025-03-04T20:02:07.3360119Z  * [new tag]                 ciflow/xpu/147583           -> ciflow/xpu/147583
2025-03-04T20:02:07.3361471Z  * [new tag]                 ciflow/xpu/147593           -> ciflow/xpu/147593
2025-03-04T20:02:07.3362337Z  * [new tag]                 ciflow/xpu/147664           -> ciflow/xpu/147664
2025-03-04T20:02:07.3363370Z  * [new tag]                 ciflow/xpu/147727           -> ciflow/xpu/147727
2025-03-04T20:02:07.3364301Z  * [new tag]                 ciflow/xpu/147821           -> ciflow/xpu/147821
2025-03-04T20:02:07.3365578Z  * [new tag]                 ciflow/xpu/147945           -> ciflow/xpu/147945
2025-03-04T20:02:07.3366453Z  * [new tag]                 ciflow/xpu/147955           -> ciflow/xpu/147955
2025-03-04T20:02:07.3367471Z  * [new tag]                 ciflow/xpu/147956           -> ciflow/xpu/147956
2025-03-04T20:02:07.3368457Z  * [new tag]                 ciflow/xpu/147957           -> ciflow/xpu/147957
2025-03-04T20:02:07.3369542Z  * [new tag]                 ciflow/xpu/147958           -> ciflow/xpu/147958
2025-03-04T20:02:07.3370491Z  * [new tag]                 ciflow/xpu/147959           -> ciflow/xpu/147959
2025-03-04T20:02:07.3371462Z  * [new tag]                 ciflow/xpu/147962           -> ciflow/xpu/147962
2025-03-04T20:02:07.3372465Z  * [new tag]                 ciflow/xpu/148076           -> ciflow/xpu/148076
2025-03-04T20:02:07.3374290Z  * [new tag]                 ciflow/xpu/148081           -> ciflow/xpu/148081
2025-03-04T20:02:07.3379101Z  * [new tag]                 ciflow/xpu/148305           -> ciflow/xpu/148305
2025-03-04T20:02:07.3379819Z  * [new tag]                 ciflow/xpu/148313           -> ciflow/xpu/148313
2025-03-04T20:02:07.3380879Z  * [new tag]                 ciflow/xpu/148366           -> ciflow/xpu/148366
2025-03-04T20:02:07.3381863Z  * [new tag]                 ciflow/xpu/148403           -> ciflow/xpu/148403
2025-03-04T20:02:07.3383059Z  * [new tag]                 ciflow/xpu/148423           -> ciflow/xpu/148423
2025-03-04T20:02:07.3384371Z  * [new tag]                 cslpull75                   -> cslpull75
2025-03-04T20:02:07.3385424Z  * [new tag]                 cslpull76                   -> cslpull76
2025-03-04T20:02:07.3386659Z  * [new tag]                 cslpull77                   -> cslpull77
2025-03-04T20:02:07.3387667Z  * [new tag]                 cslpull78                   -> cslpull78
2025-03-04T20:02:07.3389266Z  * [new tag]                 cslpull79                   -> cslpull79
2025-03-04T20:02:07.3390680Z  * [new tag]                 cslpull80                   -> cslpull80
2025-03-04T20:02:07.3391761Z  * [new tag]                 cslpull81                   -> cslpull81
2025-03-04T20:02:07.3393222Z  * [new tag]                 cslpull82                   -> cslpull82
2025-03-04T20:02:07.3394267Z  * [new tag]                 cslpull83                   -> cslpull83
2025-03-04T20:02:07.3395738Z  * [new tag]                 cslpull84                   -> cslpull84
2025-03-04T20:02:07.3396551Z  * [new tag]                 cslpull85                   -> cslpull85
2025-03-04T20:02:07.3397930Z  * [new tag]                 cslpull86                   -> cslpull86
2025-03-04T20:02:07.3399299Z  * [new tag]                 cslpull87                   -> cslpull87
2025-03-04T20:02:07.3400350Z  * [new tag]                 cslpull88                   -> cslpull88
2025-03-04T20:02:07.3401607Z  * [new tag]                 cslpull89                   -> cslpull89
2025-03-04T20:02:07.3402423Z  * [new tag]                 cslpull90                   -> cslpull90
2025-03-04T20:02:07.3404317Z  * [new tag]                 cslpull91                   -> cslpull91
2025-03-04T20:02:07.3405190Z  * [new tag]                 cslpull92                   -> cslpull92
2025-03-04T20:02:07.3406451Z  * [new tag]                 flight_5                    -> flight_5
2025-03-04T20:02:07.3407713Z  * [new tag]                 flight_5.1                  -> flight_5.1
2025-03-04T20:02:07.3408960Z  * [new tag]                 flight_5.2                  -> flight_5.2
2025-03-04T20:02:07.3409914Z  * [new tag]                 flight_5.3                  -> flight_5.3
2025-03-04T20:02:07.3411203Z  * [new tag]                 forpull1                    -> forpull1
2025-03-04T20:02:07.3412698Z  * [new tag]                 malfet/tag-2ef5611          -> malfet/tag-2ef5611
2025-03-04T20:02:07.3413747Z  * [new tag]                 malfet/tag-317b1a0          -> malfet/tag-317b1a0
2025-03-04T20:02:07.3415015Z  * [new tag]                 malfet/tag-ec6f767          -> malfet/tag-ec6f767
2025-03-04T20:02:07.3416323Z  * [new tag]                 nightly-binary              -> nightly-binary
2025-03-04T20:02:07.3417227Z  * [new tag]                 sqzhang_flight4_plus        -> sqzhang_flight4_plus
2025-03-04T20:02:07.3418818Z  * [new tag]                 sqzhang_flight_3            -> sqzhang_flight_3
2025-03-04T20:02:07.3419680Z  * [new tag]                 v0.1.1                      -> v0.1.1
2025-03-04T20:02:07.3421262Z  * [new tag]                 v0.1.10                     -> v0.1.10
2025-03-04T20:02:07.3422196Z  * [new tag]                 v0.1.11                     -> v0.1.11
2025-03-04T20:02:07.3423519Z  * [new tag]                 v0.1.12                     -> v0.1.12
2025-03-04T20:02:07.3424429Z  * [new tag]                 v0.1.2                      -> v0.1.2
2025-03-04T20:02:07.3425651Z  * [new tag]                 v0.1.3                      -> v0.1.3
2025-03-04T20:02:07.3426696Z  * [new tag]                 v0.1.4                      -> v0.1.4
2025-03-04T20:02:07.3427973Z  * [new tag]                 v0.1.5                      -> v0.1.5
2025-03-04T20:02:07.3429677Z  * [new tag]                 v0.1.6                      -> v0.1.6
2025-03-04T20:02:07.3430519Z  * [new tag]                 v0.1.7                      -> v0.1.7
2025-03-04T20:02:07.3431689Z  * [new tag]                 v0.1.8                      -> v0.1.8
2025-03-04T20:02:07.3432711Z  * [new tag]                 v0.1.9                      -> v0.1.9
2025-03-04T20:02:07.3434019Z  * [new tag]                 v0.2.0                      -> v0.2.0
2025-03-04T20:02:07.3435046Z  * [new tag]                 v0.3.0                      -> v0.3.0
2025-03-04T20:02:07.3436788Z  * [new tag]                 v0.3.1                      -> v0.3.1
2025-03-04T20:02:07.3437456Z  * [new tag]                 v0.4.0                      -> v0.4.0
2025-03-04T20:02:07.3438742Z  * [new tag]                 v0.4.1                      -> v0.4.1
2025-03-04T20:02:07.3439733Z  * [new tag]                 v1.0.0                      -> v1.0.0
2025-03-04T20:02:07.3441156Z  * [new tag]                 v1.0.0a0                    -> v1.0.0a0
2025-03-04T20:02:07.3442127Z  * [new tag]                 v1.0.1                      -> v1.0.1
2025-03-04T20:02:07.3443423Z  * [new tag]                 v1.0rc0                     -> v1.0rc0
2025-03-04T20:02:07.3444326Z  * [new tag]                 v1.0rc1                     -> v1.0rc1
2025-03-04T20:02:07.3445516Z  * [new tag]                 v1.1.0                      -> v1.1.0
2025-03-04T20:02:07.3446813Z  * [new tag]                 v1.1.0a0                    -> v1.1.0a0
2025-03-04T20:02:07.3448263Z  * [new tag]                 v1.10.0                     -> v1.10.0
2025-03-04T20:02:07.3449468Z  * [new tag]                 v1.10.0-rc1                 -> v1.10.0-rc1
2025-03-04T20:02:07.3450793Z  * [new tag]                 v1.10.0-rc2                 -> v1.10.0-rc2
2025-03-04T20:02:07.3451605Z  * [new tag]                 v1.10.0-rc3                 -> v1.10.0-rc3
2025-03-04T20:02:07.3453043Z  * [new tag]                 v1.10.1                     -> v1.10.1
2025-03-04T20:02:07.3453826Z  * [new tag]                 v1.10.1-rc1                 -> v1.10.1-rc1
2025-03-04T20:02:07.3454719Z  * [new tag]                 v1.10.2                     -> v1.10.2
2025-03-04T20:02:07.3455649Z  * [new tag]                 v1.10.2-rc1                 -> v1.10.2-rc1
2025-03-04T20:02:07.3457035Z  * [new tag]                 v1.11.0                     -> v1.11.0
2025-03-04T20:02:07.3458568Z  * [new tag]                 v1.11.0-rc1                 -> v1.11.0-rc1
2025-03-04T20:02:07.3459903Z  * [new tag]                 v1.11.0-rc2                 -> v1.11.0-rc2
2025-03-04T20:02:07.3461204Z  * [new tag]                 v1.11.0-rc3                 -> v1.11.0-rc3
2025-03-04T20:02:07.3462397Z  * [new tag]                 v1.11.0-rc4                 -> v1.11.0-rc4
2025-03-04T20:02:07.3463764Z  * [new tag]                 v1.11.0-rc5                 -> v1.11.0-rc5
2025-03-04T20:02:07.3464583Z  * [new tag]                 v1.11.0-rc6                 -> v1.11.0-rc6
2025-03-04T20:02:07.3465485Z  * [new tag]                 v1.11.0-rc7                 -> v1.11.0-rc7
2025-03-04T20:02:07.3466942Z  * [new tag]                 v1.12.0                     -> v1.12.0
2025-03-04T20:02:07.3467996Z  * [new tag]                 v1.12.0-rc1                 -> v1.12.0-rc1
2025-03-04T20:02:07.3469458Z  * [new tag]                 v1.12.0-rc2                 -> v1.12.0-rc2
2025-03-04T20:02:07.3470698Z  * [new tag]                 v1.12.0-rc3                 -> v1.12.0-rc3
2025-03-04T20:02:07.3472014Z  * [new tag]                 v1.12.0-rc4                 -> v1.12.0-rc4
2025-03-04T20:02:07.3473263Z  * [new tag]                 v1.12.0-rc5                 -> v1.12.0-rc5
2025-03-04T20:02:07.3474872Z  * [new tag]                 v1.12.0-rc6                 -> v1.12.0-rc6
2025-03-04T20:02:07.3475672Z  * [new tag]                 v1.12.0-rc7                 -> v1.12.0-rc7
2025-03-04T20:02:07.3476596Z  * [new tag]                 v1.12.0-rc8                 -> v1.12.0-rc8
2025-03-04T20:02:07.3477543Z  * [new tag]                 v1.12.1                     -> v1.12.1
2025-03-04T20:02:07.3479077Z  * [new tag]                 v1.12.1-rc1                 -> v1.12.1-rc1
2025-03-04T20:02:07.3480144Z  * [new tag]                 v1.12.1-rc2                 -> v1.12.1-rc2
2025-03-04T20:02:07.3481607Z  * [new tag]                 v1.12.1-rc3                 -> v1.12.1-rc3
2025-03-04T20:02:07.3482818Z  * [new tag]                 v1.12.1-rc4                 -> v1.12.1-rc4
2025-03-04T20:02:07.3483621Z  * [new tag]                 v1.12.1-rc5                 -> v1.12.1-rc5
2025-03-04T20:02:07.3485088Z  * [new tag]                 v1.13.0                     -> v1.13.0
2025-03-04T20:02:07.3486105Z  * [new tag]                 v1.13.0-rc1                 -> v1.13.0-rc1
2025-03-04T20:02:07.3487499Z  * [new tag]                 v1.13.0-rc2                 -> v1.13.0-rc2
2025-03-04T20:02:07.3488538Z  * [new tag]                 v1.13.0-rc3                 -> v1.13.0-rc3
2025-03-04T20:02:07.3490047Z  * [new tag]                 v1.13.0-rc4                 -> v1.13.0-rc4
2025-03-04T20:02:07.3490772Z  * [new tag]                 v1.13.0-rc5                 -> v1.13.0-rc5
2025-03-04T20:02:07.3491684Z  * [new tag]                 v1.13.0-rc6                 -> v1.13.0-rc6
2025-03-04T20:02:07.3493320Z  * [new tag]                 v1.13.1                     -> v1.13.1
2025-03-04T20:02:07.3493892Z  * [new tag]                 v1.13.1-rc1                 -> v1.13.1-rc1
2025-03-04T20:02:07.3495243Z  * [new tag]                 v1.2.0                      -> v1.2.0
2025-03-04T20:02:07.3496524Z  * [new tag]                 v1.2.0a0                    -> v1.2.0a0
2025-03-04T20:02:07.3497604Z  * [new tag]                 v1.3.0                      -> v1.3.0
2025-03-04T20:02:07.3499092Z  * [new tag]                 v1.3.0a0                    -> v1.3.0a0
2025-03-04T20:02:07.3499782Z  * [new tag]                 v1.3.1                      -> v1.3.1
2025-03-04T20:02:07.3501113Z  * [new tag]                 v1.4.0                      -> v1.4.0
2025-03-04T20:02:07.3502145Z  * [new tag]                 v1.4.0a0                    -> v1.4.0a0
2025-03-04T20:02:07.3503070Z  * [new tag]                 v1.4.1                      -> v1.4.1
2025-03-04T20:02:07.3504964Z  * [new tag]                 v1.5.0                      -> v1.5.0
2025-03-04T20:02:07.3506022Z  * [new tag]                 v1.5.0-rc1                  -> v1.5.0-rc1
2025-03-04T20:02:07.3507411Z  * [new tag]                 v1.5.0-rc2                  -> v1.5.0-rc2
2025-03-04T20:02:07.3508802Z  * [new tag]                 v1.5.0-rc3                  -> v1.5.0-rc3
2025-03-04T20:02:07.3509707Z  * [new tag]                 v1.5.0-rc4                  -> v1.5.0-rc4
2025-03-04T20:02:07.3510638Z  * [new tag]                 v1.5.0-rc5                  -> v1.5.0-rc5
2025-03-04T20:02:07.3512038Z  * [new tag]                 v1.5.1                      -> v1.5.1
2025-03-04T20:02:07.3512888Z  * [new tag]                 v1.5.1-rc1                  -> v1.5.1-rc1
2025-03-04T20:02:07.3513822Z  * [new tag]                 v1.6.0                      -> v1.6.0
2025-03-04T20:02:07.3515249Z  * [new tag]                 v1.6.0-rc1                  -> v1.6.0-rc1
2025-03-04T20:02:07.3516727Z  * [new tag]                 v1.6.0-rc2                  -> v1.6.0-rc2
2025-03-04T20:02:07.3517855Z  * [new tag]                 v1.6.0-rc3                  -> v1.6.0-rc3
2025-03-04T20:02:07.3519287Z  * [new tag]                 v1.6.0-rc4                  -> v1.6.0-rc4
2025-03-04T20:02:07.3520380Z  * [new tag]                 v1.6.0-rc5                  -> v1.6.0-rc5
2025-03-04T20:02:07.3521848Z  * [new tag]                 v1.6.0-rc6                  -> v1.6.0-rc6
2025-03-04T20:02:07.3522605Z  * [new tag]                 v1.6.0-rc7                  -> v1.6.0-rc7
2025-03-04T20:02:07.3524029Z  * [new tag]                 v1.7.0                      -> v1.7.0
2025-03-04T20:02:07.3525089Z  * [new tag]                 v1.7.0-rc1                  -> v1.7.0-rc1
2025-03-04T20:02:07.3526652Z  * [new tag]                 v1.7.0-rc2                  -> v1.7.0-rc2
2025-03-04T20:02:07.3527683Z  * [new tag]                 v1.7.0-rc3                  -> v1.7.0-rc3
2025-03-04T20:02:07.3528600Z  * [new tag]                 v1.7.0-rc4                  -> v1.7.0-rc4
2025-03-04T20:02:07.3530008Z  * [new tag]                 v1.7.1                      -> v1.7.1
2025-03-04T20:02:07.3531412Z  * [new tag]                 v1.7.1-rc1                  -> v1.7.1-rc1
2025-03-04T20:02:07.3532714Z  * [new tag]                 v1.7.1-rc2                  -> v1.7.1-rc2
2025-03-04T20:02:07.3533525Z  * [new tag]                 v1.7.1-rc3                  -> v1.7.1-rc3
2025-03-04T20:02:07.3535099Z  * [new tag]                 v1.8.0                      -> v1.8.0
2025-03-04T20:02:07.3535853Z  * [new tag]                 v1.8.0-rc1                  -> v1.8.0-rc1
2025-03-04T20:02:07.3537301Z  * [new tag]                 v1.8.0-rc2                  -> v1.8.0-rc2
2025-03-04T20:02:07.3538552Z  * [new tag]                 v1.8.0-rc3                  -> v1.8.0-rc3
2025-03-04T20:02:07.3539772Z  * [new tag]                 v1.8.0-rc4                  -> v1.8.0-rc4
2025-03-04T20:02:07.3540592Z  * [new tag]                 v1.8.0-rc5                  -> v1.8.0-rc5
2025-03-04T20:02:07.3541543Z  * [new tag]                 v1.8.1                      -> v1.8.1
2025-03-04T20:02:07.3543026Z  * [new tag]                 v1.8.1-rc1                  -> v1.8.1-rc1
2025-03-04T20:02:07.3543653Z  * [new tag]                 v1.8.1-rc2                  -> v1.8.1-rc2
2025-03-04T20:02:07.3544610Z  * [new tag]                 v1.8.1-rc3                  -> v1.8.1-rc3
2025-03-04T20:02:07.3546622Z  * [new tag]                 v1.8.2                      -> v1.8.2
2025-03-04T20:02:07.3547303Z  * [new tag]                 v1.8.2-rc1                  -> v1.8.2-rc1
2025-03-04T20:02:07.3548724Z  * [new tag]                 v1.9.0                      -> v1.9.0
2025-03-04T20:02:07.3550295Z  * [new tag]                 v1.9.0-rc1                  -> v1.9.0-rc1
2025-03-04T20:02:07.3551060Z  * [new tag]                 v1.9.0-rc2                  -> v1.9.0-rc2
2025-03-04T20:02:07.3552548Z  * [new tag]                 v1.9.0-rc3                  -> v1.9.0-rc3
2025-03-04T20:02:07.3553290Z  * [new tag]                 v1.9.0-rc4                  -> v1.9.0-rc4
2025-03-04T20:02:07.3554723Z  * [new tag]                 v1.9.1                      -> v1.9.1
2025-03-04T20:02:07.3556233Z  * [new tag]                 v1.9.1-rc1                  -> v1.9.1-rc1
2025-03-04T20:02:07.3556960Z  * [new tag]                 v1.9.1-rc2                  -> v1.9.1-rc2
2025-03-04T20:02:07.3558429Z  * [new tag]                 v2.0.0                      -> v2.0.0
2025-03-04T20:02:07.3559417Z  * [new tag]                 v2.0.0-rc1                  -> v2.0.0-rc1
2025-03-04T20:02:07.3560854Z  * [new tag]                 v2.0.0-rc2                  -> v2.0.0-rc2
2025-03-04T20:02:07.3562094Z  * [new tag]                 v2.0.0-rc3                  -> v2.0.0-rc3
2025-03-04T20:02:07.3563356Z  * [new tag]                 v2.0.0-rc4                  -> v2.0.0-rc4
2025-03-04T20:02:07.3564641Z  * [new tag]                 v2.0.0-rc5                  -> v2.0.0-rc5
2025-03-04T20:02:07.3565499Z  * [new tag]                 v2.0.0-rc6                  -> v2.0.0-rc6
2025-03-04T20:02:07.3566977Z  * [new tag]                 v2.0.1                      -> v2.0.1
2025-03-04T20:02:07.3568252Z  * [new tag]                 v2.0.1-rc1                  -> v2.0.1-rc1
2025-03-04T20:02:07.3569235Z  * [new tag]                 v2.0.1-rc2                  -> v2.0.1-rc2
2025-03-04T20:02:07.3570549Z  * [new tag]                 v2.0.1-rc3                  -> v2.0.1-rc3
2025-03-04T20:02:07.3571363Z  * [new tag]                 v2.0.1-rc4                  -> v2.0.1-rc4
2025-03-04T20:02:07.3573435Z  * [new tag]                 v2.1.0                      -> v2.1.0
2025-03-04T20:02:07.3580302Z  * [new tag]                 v2.1.0-rc1                  -> v2.1.0-rc1
2025-03-04T20:02:07.3581277Z  * [new tag]                 v2.1.0-rc2                  -> v2.1.0-rc2
2025-03-04T20:02:07.3582768Z  * [new tag]                 v2.1.0-rc3                  -> v2.1.0-rc3
2025-03-04T20:02:07.3583995Z  * [new tag]                 v2.1.0-rc4                  -> v2.1.0-rc4
2025-03-04T20:02:07.3585333Z  * [new tag]                 v2.1.0-rc5                  -> v2.1.0-rc5
2025-03-04T20:02:07.3586112Z  * [new tag]                 v2.1.0-rc6                  -> v2.1.0-rc6
2025-03-04T20:02:07.3587894Z  * [new tag]                 v2.1.1                      -> v2.1.1
2025-03-04T20:02:07.3589114Z  * [new tag]                 v2.1.1-rc1                  -> v2.1.1-rc1
2025-03-04T20:02:07.3590333Z  * [new tag]                 v2.1.1-rc2                  -> v2.1.1-rc2
2025-03-04T20:02:07.3591710Z  * [new tag]                 v2.1.1-rc3                  -> v2.1.1-rc3
2025-03-04T20:02:07.3592885Z  * [new tag]                 v2.1.1-rc4                  -> v2.1.1-rc4
2025-03-04T20:02:07.3594172Z  * [new tag]                 v2.1.1-rc5                  -> v2.1.1-rc5
2025-03-04T20:02:07.3594981Z  * [new tag]                 v2.1.1-rc6                  -> v2.1.1-rc6
2025-03-04T20:02:07.3596308Z  * [new tag]                 v2.1.2                      -> v2.1.2
2025-03-04T20:02:07.3597576Z  * [new tag]                 v2.1.2-rc1                  -> v2.1.2-rc1
2025-03-04T20:02:07.3598992Z  * [new tag]                 v2.1.2-rc2                  -> v2.1.2-rc2
2025-03-04T20:02:07.3599716Z  * [new tag]                 v2.1.2-rc3                  -> v2.1.2-rc3
2025-03-04T20:02:07.3601216Z  * [new tag]                 v2.2.0                      -> v2.2.0
2025-03-04T20:02:07.3602247Z  * [new tag]                 v2.2.0-rc1                  -> v2.2.0-rc1
2025-03-04T20:02:07.3603532Z  * [new tag]                 v2.2.0-rc2                  -> v2.2.0-rc2
2025-03-04T20:02:07.3604597Z  * [new tag]                 v2.2.0-rc3                  -> v2.2.0-rc3
2025-03-04T20:02:07.3605890Z  * [new tag]                 v2.2.0-rc4                  -> v2.2.0-rc4
2025-03-04T20:02:07.3606988Z  * [new tag]                 v2.2.0-rc5                  -> v2.2.0-rc5
2025-03-04T20:02:07.3608342Z  * [new tag]                 v2.2.0-rc6                  -> v2.2.0-rc6
2025-03-04T20:02:07.3609149Z  * [new tag]                 v2.2.0-rc7                  -> v2.2.0-rc7
2025-03-04T20:02:07.3610082Z  * [new tag]                 v2.2.0-rc8                  -> v2.2.0-rc8
2025-03-04T20:02:07.3611503Z  * [new tag]                 v2.2.1                      -> v2.2.1
2025-03-04T20:02:07.3612751Z  * [new tag]                 v2.2.1-rc1                  -> v2.2.1-rc1
2025-03-04T20:02:07.3613612Z  * [new tag]                 v2.2.1-rc2                  -> v2.2.1-rc2
2025-03-04T20:02:07.3614517Z  * [new tag]                 v2.2.1-rc3                  -> v2.2.1-rc3
2025-03-04T20:02:07.3615435Z  * [new tag]                 v2.2.2                      -> v2.2.2
2025-03-04T20:02:07.3617068Z  * [new tag]                 v2.2.2-rc1                  -> v2.2.2-rc1
2025-03-04T20:02:07.3617839Z  * [new tag]                 v2.2.2-rc2                  -> v2.2.2-rc2
2025-03-04T20:02:07.3618902Z  * [new tag]                 v2.2.2-rc3                  -> v2.2.2-rc3
2025-03-04T20:02:07.3620265Z  * [new tag]                 v2.3.0                      -> v2.3.0
2025-03-04T20:02:07.3621525Z  * [new tag]                 v2.3.0-rc1                  -> v2.3.0-rc1
2025-03-04T20:02:07.3622695Z  * [new tag]                 v2.3.0-rc10                 -> v2.3.0-rc10
2025-03-04T20:02:07.3624236Z  * [new tag]                 v2.3.0-rc11                 -> v2.3.0-rc11
2025-03-04T20:02:07.3624926Z  * [new tag]                 v2.3.0-rc12                 -> v2.3.0-rc12
2025-03-04T20:02:07.3626468Z  * [new tag]                 v2.3.0-rc2                  -> v2.3.0-rc2
2025-03-04T20:02:07.3627531Z  * [new tag]                 v2.3.0-rc3                  -> v2.3.0-rc3
2025-03-04T20:02:07.3628881Z  * [new tag]                 v2.3.0-rc4                  -> v2.3.0-rc4
2025-03-04T20:02:07.3630151Z  * [new tag]                 v2.3.0-rc5                  -> v2.3.0-rc5
2025-03-04T20:02:07.3630960Z  * [new tag]                 v2.3.0-rc6                  -> v2.3.0-rc6
2025-03-04T20:02:07.3632325Z  * [new tag]                 v2.3.0-rc7                  -> v2.3.0-rc7
2025-03-04T20:02:07.3633584Z  * [new tag]                 v2.3.0-rc8                  -> v2.3.0-rc8
2025-03-04T20:02:07.3634425Z  * [new tag]                 v2.3.0-rc9                  -> v2.3.0-rc9
2025-03-04T20:02:07.3635376Z  * [new tag]                 v2.3.1                      -> v2.3.1
2025-03-04T20:02:07.3636849Z  * [new tag]                 v2.3.1-rc1                  -> v2.3.1-rc1
2025-03-04T20:02:07.3638015Z  * [new tag]                 v2.3.1-rc2                  -> v2.3.1-rc2
2025-03-04T20:02:07.3639325Z  * [new tag]                 v2.3.1-rc3                  -> v2.3.1-rc3
2025-03-04T20:02:07.3640627Z  * [new tag]                 v2.4.0                      -> v2.4.0
2025-03-04T20:02:07.3641854Z  * [new tag]                 v2.4.0-rc1                  -> v2.4.0-rc1
2025-03-04T20:02:07.3643065Z  * [new tag]                 v2.4.0-rc2                  -> v2.4.0-rc2
2025-03-04T20:02:07.3644393Z  * [new tag]                 v2.4.0-rc3                  -> v2.4.0-rc3
2025-03-04T20:02:07.3645447Z  * [new tag]                 v2.4.0-rc4                  -> v2.4.0-rc4
2025-03-04T20:02:07.3647066Z  * [new tag]                 v2.4.0-rc5                  -> v2.4.0-rc5
2025-03-04T20:02:07.3648134Z  * [new tag]                 v2.4.0-rc6                  -> v2.4.0-rc6
2025-03-04T20:02:07.3649541Z  * [new tag]                 v2.4.0-rc7                  -> v2.4.0-rc7
2025-03-04T20:02:07.3650728Z  * [new tag]                 v2.4.0-rc8                  -> v2.4.0-rc8
2025-03-04T20:02:07.3652156Z  * [new tag]                 v2.4.0-rc9                  -> v2.4.0-rc9
2025-03-04T20:02:07.3652974Z  * [new tag]                 v2.4.1                      -> v2.4.1
2025-03-04T20:02:07.3654507Z  * [new tag]                 v2.4.1-rc1                  -> v2.4.1-rc1
2025-03-04T20:02:07.3655889Z  * [new tag]                 v2.4.1-rc2                  -> v2.4.1-rc2
2025-03-04T20:02:07.3657141Z  * [new tag]                 v2.4.1-rc3                  -> v2.4.1-rc3
2025-03-04T20:02:07.3658584Z  * [new tag]                 v2.5.0                      -> v2.5.0
2025-03-04T20:02:07.3659965Z  * [new tag]                 v2.5.0-rc1                  -> v2.5.0-rc1
2025-03-04T20:02:07.3660796Z  * [new tag]                 v2.5.0-rc10                 -> v2.5.0-rc10
2025-03-04T20:02:07.3662347Z  * [new tag]                 v2.5.0-rc2                  -> v2.5.0-rc2
2025-03-04T20:02:07.3663093Z  * [new tag]                 v2.5.0-rc3                  -> v2.5.0-rc3
2025-03-04T20:02:07.3664309Z  * [new tag]                 v2.5.0-rc4                  -> v2.5.0-rc4
2025-03-04T20:02:07.3665075Z  * [new tag]                 v2.5.0-rc5                  -> v2.5.0-rc5
2025-03-04T20:02:07.3666053Z  * [new tag]                 v2.5.0-rc6                  -> v2.5.0-rc6
2025-03-04T20:02:07.3666824Z  * [new tag]                 v2.5.0-rc7                  -> v2.5.0-rc7
2025-03-04T20:02:07.3667798Z  * [new tag]                 v2.5.0-rc8                  -> v2.5.0-rc8
2025-03-04T20:02:07.3668525Z  * [new tag]                 v2.5.0-rc9                  -> v2.5.0-rc9
2025-03-04T20:02:07.3669134Z  * [new tag]                 v2.5.1                      -> v2.5.1
2025-03-04T20:02:07.3669792Z  * [new tag]                 v2.5.1-rc1                  -> v2.5.1-rc1
2025-03-04T20:02:07.3670400Z  * [new tag]                 v2.6.0                      -> v2.6.0
2025-03-04T20:02:07.3671363Z  * [new tag]                 v2.6.0-rc1                  -> v2.6.0-rc1
2025-03-04T20:02:07.3672311Z  * [new tag]                 v2.6.0-rc2                  -> v2.6.0-rc2
2025-03-04T20:02:07.3673237Z  * [new tag]                 v2.6.0-rc3                  -> v2.6.0-rc3
2025-03-04T20:02:07.3674520Z  * [new tag]                 v2.6.0-rc4                  -> v2.6.0-rc4
2025-03-04T20:02:07.3675563Z  * [new tag]                 v2.6.0-rc5                  -> v2.6.0-rc5
2025-03-04T20:02:07.3676523Z  * [new tag]                 v2.6.0-rc6                  -> v2.6.0-rc6
2025-03-04T20:02:07.3677319Z  * [new tag]                 v2.6.0-rc7                  -> v2.6.0-rc7
2025-03-04T20:02:07.3678310Z  * [new tag]                 v2.6.0-rc8                  -> v2.6.0-rc8
2025-03-04T20:02:07.3679118Z  * [new tag]                 v2.6.0-rc9                  -> v2.6.0-rc9
2025-03-04T20:02:07.3679852Z  * [new tag]                 whc_flight_1                -> whc_flight_1
2025-03-04T20:02:07.3680475Z  * [new tag]                 whc_flight_2                -> whc_flight_2
2025-03-04T20:02:07.3681271Z  * [new tag]                 whc_flight_4                -> whc_flight_4
2025-03-04T20:02:07.4231697Z [command]/usr/bin/git rev-parse --verify --quiet 1b7498080987913ecb3aff6253c5e88f3540d911^{object}
2025-03-04T20:02:07.4253585Z 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:02:07.4258089Z ##[endgroup]
2025-03-04T20:02:07.4258830Z ##[group]Determining the checkout info
2025-03-04T20:02:07.4260000Z ##[endgroup]
2025-03-04T20:02:07.4263523Z [command]/usr/bin/git sparse-checkout disable
2025-03-04T20:02:07.4295670Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig
2025-03-04T20:02:07.4317881Z ##[group]Checking out the ref
2025-03-04T20:02:07.4322035Z [command]/usr/bin/git checkout --progress --force 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:02:08.4595760Z Updating files:  99% (16404/16541)
2025-03-04T20:02:08.4596216Z Updating files: 100% (16541/16541)
2025-03-04T20:02:08.4596559Z Updating files: 100% (16541/16541), done.
2025-03-04T20:02:08.4825087Z Note: switching to '1b7498080987913ecb3aff6253c5e88f3540d911'.
2025-03-04T20:02:08.4825606Z 
2025-03-04T20:02:08.4825856Z You are in 'detached HEAD' state. You can look around, make experimental
2025-03-04T20:02:08.4826470Z changes and commit them, and you can discard any commits you make in this
2025-03-04T20:02:08.4827060Z state without impacting any branches by switching back to a branch.
2025-03-04T20:02:08.4827400Z 
2025-03-04T20:02:08.4827635Z If you want to create a new branch to retain commits you create, you may
2025-03-04T20:02:08.4828188Z do so (now or later) by using -c with the switch command. Example:
2025-03-04T20:02:08.4828499Z 
2025-03-04T20:02:08.4828635Z   git switch -c <new-branch-name>
2025-03-04T20:02:08.4828881Z 
2025-03-04T20:02:08.4829027Z Or undo this operation with:
2025-03-04T20:02:08.4829225Z 
2025-03-04T20:02:08.4829337Z   git switch -
2025-03-04T20:02:08.4829481Z 
2025-03-04T20:02:08.4829747Z Turn off this advice by setting config variable advice.detachedHead to false
2025-03-04T20:02:08.4830112Z 
2025-03-04T20:02:08.4830481Z HEAD is now at 1b749808098 Update on "[dynamo] remove internal stack trace for fullgraph=True graph breaks"
2025-03-04T20:02:08.4875468Z ##[endgroup]
2025-03-04T20:02:08.4875966Z ##[group]Setting up auth for fetching submodules
2025-03-04T20:02:08.4885718Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-04T20:02:08.4922154Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2025-03-04T20:02:08.4946456Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2025-03-04T20:02:08.4970217Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2025-03-04T20:02:08.4991000Z ##[endgroup]
2025-03-04T20:02:08.4991472Z ##[group]Fetching submodules
2025-03-04T20:02:08.4993902Z [command]/usr/bin/git submodule sync --recursive
2025-03-04T20:02:08.5268020Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2025-03-04T20:02:08.5536816Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2025-03-04T20:02:08.5538511Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2025-03-04T20:02:08.5541143Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2025-03-04T20:02:08.5544376Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2025-03-04T20:02:08.5547661Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX'
2025-03-04T20:02:08.5550876Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2025-03-04T20:02:08.5553657Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2025-03-04T20:02:08.5556910Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2025-03-04T20:02:08.5560397Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel'
2025-03-04T20:02:08.5563786Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib'
2025-03-04T20:02:08.5567261Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2025-03-04T20:02:08.5571203Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2025-03-04T20:02:08.5575378Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2025-03-04T20:02:08.5579164Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen'
2025-03-04T20:02:08.5589332Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2025-03-04T20:02:08.5590491Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention'
2025-03-04T20:02:08.5594225Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2025-03-04T20:02:08.5598061Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2025-03-04T20:02:08.5602433Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2025-03-04T20:02:08.5606733Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo'
2025-03-04T20:02:08.5611205Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2025-03-04T20:02:08.5615651Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2025-03-04T20:02:08.5620402Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2025-03-04T20:02:08.5625059Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2025-03-04T20:02:08.5629900Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai'
2025-03-04T20:02:08.5634806Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc'
2025-03-04T20:02:08.5639677Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2025-03-04T20:02:08.5644757Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2025-03-04T20:02:08.5650117Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp'
2025-03-04T20:02:08.5655142Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2025-03-04T20:02:08.5660644Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2025-03-04T20:02:08.5666020Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2025-03-04T20:02:08.5671595Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2025-03-04T20:02:08.5678726Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2025-03-04T20:02:08.5684630Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2025-03-04T20:02:08.5690508Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef'
2025-03-04T20:02:08.5696428Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe'
2025-03-04T20:02:08.5724710Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'...
2025-03-04T20:02:08.9136678Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'...
2025-03-04T20:02:08.9137715Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'...
2025-03-04T20:02:08.9138564Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'...
2025-03-04T20:02:08.9162189Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'...
2025-03-04T20:02:09.4691102Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'...
2025-03-04T20:02:09.4692937Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'...
2025-03-04T20:02:09.4727078Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'...
2025-03-04T20:02:14.0005148Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'...
2025-03-04T20:02:14.0007114Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'...
2025-03-04T20:02:14.0009109Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'...
2025-03-04T20:02:14.0010950Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'...
2025-03-04T20:02:14.0300783Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'...
2025-03-04T20:02:15.3382899Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'...
2025-03-04T20:02:15.3384488Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'...
2025-03-04T20:02:15.3385908Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'...
2025-03-04T20:02:15.3387333Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'...
2025-03-04T20:02:15.3388715Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'...
2025-03-04T20:02:15.3390087Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'...
2025-03-04T20:02:15.3415574Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'...
2025-03-04T20:02:17.0316901Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'...
2025-03-04T20:02:17.0318365Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'...
2025-03-04T20:02:17.0319850Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'...
2025-03-04T20:02:17.0344962Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'...
2025-03-04T20:02:17.3514114Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'...
2025-03-04T20:02:43.1747797Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'...
2025-03-04T20:02:43.1748585Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'...
2025-03-04T20:02:43.1749428Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'...
2025-03-04T20:02:43.1750246Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'...
2025-03-04T20:02:43.1751040Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'...
2025-03-04T20:02:43.1751792Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'...
2025-03-04T20:02:43.1752550Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'...
2025-03-04T20:02:43.1753305Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'...
2025-03-04T20:02:43.1754031Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'...
2025-03-04T20:02:43.1754785Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'...
2025-03-04T20:02:43.1755594Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'...
2025-03-04T20:02:43.1756625Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'...
2025-03-04T20:02:43.1892805Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2025-03-04T20:02:43.2005682Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2025-03-04T20:02:43.2091143Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2025-03-04T20:02:43.2320694Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2025-03-04T20:02:43.2638801Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1'
2025-03-04T20:02:43.2985262Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191'
2025-03-04T20:02:43.9876925Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883'
2025-03-04T20:02:44.0099662Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415'
2025-03-04T20:02:44.2396586Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3'
2025-03-04T20:02:44.2862424Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006'
2025-03-04T20:02:44.3817221Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99'
2025-03-04T20:02:44.4149376Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a'
2025-03-04T20:02:44.9814733Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008'
2025-03-04T20:02:45.2279873Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'
2025-03-04T20:02:45.3475465Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e'
2025-03-04T20:02:45.3493429Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit'
2025-03-04T20:02:45.3494610Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo'
2025-03-04T20:02:45.3497027Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass'
2025-03-04T20:02:45.3499639Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest'
2025-03-04T20:02:45.3502731Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch'
2025-03-04T20:02:45.3529244Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'...
2025-03-04T20:02:46.5941866Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'...
2025-03-04T20:02:46.5943756Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'...
2025-03-04T20:02:46.6942963Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'...
2025-03-04T20:02:47.8887724Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'...
2025-03-04T20:02:47.9358486Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
2025-03-04T20:02:48.0294311Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3'
2025-03-04T20:02:48.4184243Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6'
2025-03-04T20:02:48.4794997Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796'
2025-03-04T20:02:48.4917499Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1'
2025-03-04T20:02:48.5575104Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5'
2025-03-04T20:02:48.5593321Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel'
2025-03-04T20:02:48.5594908Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass'
2025-03-04T20:02:48.5620702Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'...
2025-03-04T20:02:51.1377852Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'...
2025-03-04T20:02:51.3795801Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33'
2025-03-04T20:02:51.9229441Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420'
2025-03-04T20:02:52.0538078Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b'
2025-03-04T20:02:52.0858287Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a'
2025-03-04T20:02:52.1248229Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2025-03-04T20:02:52.1501887Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509'
2025-03-04T20:02:52.1930426Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59'
2025-03-04T20:02:52.2050483Z Submodule path 'third_party/ideep': checked out 'e026f3b0318087fe19e2b062e8edf55bfe7a522c'
2025-03-04T20:02:52.2065250Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn'
2025-03-04T20:02:52.2088247Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'...
2025-03-04T20:03:06.5310710Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af'
2025-03-04T20:03:06.5488930Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42'
2025-03-04T20:03:06.6329858Z Submodule path 'third_party/kineto': checked out 'a054a4be0db117c579a21747debf19c863631f26'
2025-03-04T20:03:06.6347194Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-04T20:03:06.6349329Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt'
2025-03-04T20:03:06.6352064Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest'
2025-03-04T20:03:06.6377808Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'...
2025-03-04T20:03:07.4413550Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'...
2025-03-04T20:03:08.4722001Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'...
2025-03-04T20:03:08.5529023Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2025-03-04T20:03:08.5545417Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-04T20:03:08.5547296Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-04T20:03:08.5550447Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-04T20:03:08.5553588Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-04T20:03:08.5560047Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-04T20:03:08.5561450Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-04T20:03:08.5563157Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-04T20:03:08.5565765Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-04T20:03:08.5593681Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'...
2025-03-04T20:03:10.2330528Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'...
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2025-03-04T20:03:10.9871057Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'...
2025-03-04T20:03:16.7188984Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2025-03-04T20:03:16.7366038Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2025-03-04T20:03:16.7722904Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2025-03-04T20:03:16.7848679Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2025-03-04T20:03:16.7862978Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-04T20:03:16.7886614Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'...
2025-03-04T20:03:17.1091672Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2025-03-04T20:03:17.1267180Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2025-03-04T20:03:17.1654676Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2025-03-04T20:03:17.2592065Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2025-03-04T20:03:17.2749828Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2025-03-04T20:03:17.3113776Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2025-03-04T20:03:17.3682348Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2025-03-04T20:03:17.4026193Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6'
2025-03-04T20:03:17.4374562Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78'
2025-03-04T20:03:17.5425834Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806'
2025-03-04T20:03:17.8845043Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c'
2025-03-04T20:03:17.8881022Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11'
2025-03-04T20:03:17.8905936Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'...
2025-03-04T20:03:18.9176740Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b'
2025-03-04T20:03:18.9834080Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2025-03-04T20:03:18.9852482Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-04T20:03:18.9854335Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-04T20:03:18.9856185Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-04T20:03:18.9858789Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-04T20:03:18.9861384Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-04T20:03:18.9863682Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-04T20:03:18.9866323Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T20:03:18.9868811Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T20:03:18.9896476Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'...
2025-03-04T20:03:19.6423461Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'...
2025-03-04T20:03:19.6425725Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'...
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2025-03-04T20:03:29.2764830Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2025-03-04T20:03:29.3157372Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
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2025-03-04T20:03:29.4728804Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-04T20:03:29.4730928Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
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2025-03-04T20:03:32.3098691Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
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2025-03-04T20:03:33.0679950Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark'
2025-03-04T20:03:33.0682028Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest'
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2025-03-04T20:03:34.5041932Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop'
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2025-03-04T20:03:37.3968937Z ##[endgroup]
2025-03-04T20:03:37.3969526Z ##[group]Persisting credentials for submodules
2025-03-04T20:03:37.3976949Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
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2025-03-04T20:03:37.7899325Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-04T20:03:37.7947446Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T20:03:37.7994931Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T20:03:37.8057888Z [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"
2025-03-04T20:03:37.8324250Z Entering 'android/libs/fbjni'
2025-03-04T20:03:37.8370208Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2025-03-04T20:03:37.8384586Z Entering 'third_party/FP16'
2025-03-04T20:03:37.8430172Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2025-03-04T20:03:37.8443830Z Entering 'third_party/FXdiv'
2025-03-04T20:03:37.8490081Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2025-03-04T20:03:37.8504499Z Entering 'third_party/NNPACK'
2025-03-04T20:03:37.8549490Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2025-03-04T20:03:37.8564932Z Entering 'third_party/NVTX'
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2025-03-04T20:03:37.8671859Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
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2025-03-04T20:03:37.8733544Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
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2025-03-04T20:03:37.8998334Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
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2025-03-04T20:03:37.9188919Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
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2025-03-04T20:03:37.9250857Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
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2025-03-04T20:03:37.9310168Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
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2025-03-04T20:03:37.9370394Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
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2025-03-04T20:03:37.9495468Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
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2025-03-04T20:03:37.9554117Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
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2025-03-04T20:03:37.9676655Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config	remote.origin.url
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2025-03-04T20:03:37.9742078Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config	remote.origin.url
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2025-03-04T20:03:37.9810118Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2025-03-04T20:03:37.9827343Z Entering 'third_party/fmt'
2025-03-04T20:03:37.9872556Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2025-03-04T20:03:37.9887202Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-04T20:03:37.9932670Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
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2025-03-04T20:03:37.9992262Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
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2025-03-04T20:03:38.0051682Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
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2025-03-04T20:03:38.0111470Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
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2025-03-04T20:03:38.0169392Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
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2025-03-04T20:03:38.0236471Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2025-03-04T20:03:38.0250806Z Entering 'third_party/kineto'
2025-03-04T20:03:38.0296580Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2025-03-04T20:03:38.0310837Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-04T20:03:38.0356597Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config	remote.origin.url
2025-03-04T20:03:38.0369917Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-04T20:03:38.0416901Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config	remote.origin.url
2025-03-04T20:03:38.0432274Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-04T20:03:38.0478596Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config	remote.origin.url
2025-03-04T20:03:38.0492618Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-04T20:03:38.0538611Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config	remote.origin.url
2025-03-04T20:03:38.0552720Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-04T20:03:38.0598291Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config	remote.origin.url
2025-03-04T20:03:38.0611435Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-04T20:03:38.0657381Z 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
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2025-03-04T20:03:38.0718392Z 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
2025-03-04T20:03:38.0732680Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-04T20:03:38.0778014Z 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
2025-03-04T20:03:38.0792138Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-04T20:03:38.0837538Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config	remote.origin.url
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2025-03-04T20:03:38.8825568Z Entering 'third_party/nlohmann'
2025-03-04T20:03:38.8864480Z Entering 'third_party/onnx'
2025-03-04T20:03:38.8918821Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-04T20:03:38.8958881Z Entering 'third_party/opentelemetry-cpp'
2025-03-04T20:03:38.8999422Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-04T20:03:38.9036613Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-04T20:03:38.9073221Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-04T20:03:38.9109646Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-04T20:03:38.9147464Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-04T20:03:38.9183952Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-04T20:03:38.9220228Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T20:03:38.9256307Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-04T20:03:38.9296039Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-04T20:03:38.9334396Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T20:03:38.9395128Z Entering 'third_party/pocketfft'
2025-03-04T20:03:38.9434220Z Entering 'third_party/protobuf'
2025-03-04T20:03:38.9478117Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-04T20:03:38.9516053Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-04T20:03:38.9554840Z Entering 'third_party/psimd'
2025-03-04T20:03:38.9592738Z Entering 'third_party/pthreadpool'
2025-03-04T20:03:38.9630527Z Entering 'third_party/pybind11'
2025-03-04T20:03:38.9668694Z Entering 'third_party/python-peachpy'
2025-03-04T20:03:38.9707968Z Entering 'third_party/sleef'
2025-03-04T20:03:38.9746377Z Entering 'third_party/tensorpipe'
2025-03-04T20:03:38.9785546Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-04T20:03:38.9822965Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-04T20:03:38.9860157Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-04T20:03:38.9899562Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T20:03:38.9936201Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T20:03:38.9990688Z ##[endgroup]
2025-03-04T20:03:39.0022422Z [command]/usr/bin/git log -1 --format=%H
2025-03-04T20:03:39.0043886Z 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:03:39.0209803Z Prepare all required actions
2025-03-04T20:03:39.0210434Z Getting action download info
2025-03-04T20:03:39.1741445Z ##[group]Run ./.github/actions/setup-linux
2025-03-04T20:03:39.1741809Z env:
2025-03-04T20:03:39.1742060Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:39.1742351Z ##[endgroup]
2025-03-04T20:03:39.1787638Z ##[group]Run set -euo pipefail
2025-03-04T20:03:39.1788032Z set -euo pipefail
2025-03-04T20:03:39.1788360Z function get_ec2_metadata() {
2025-03-04T20:03:39.1788774Z   # Pulled from instance metadata endpoint for EC2
2025-03-04T20:03:39.1789462Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2025-03-04T20:03:39.1790067Z   category=$1
2025-03-04T20:03:39.1790463Z   # If it is GCP runner (runner name contains gcp), do not run this
2025-03-04T20:03:39.1790938Z   runner_name_str=i-0f1aad72ac3d41ea9
2025-03-04T20:03:39.1791356Z   if [[ -f /.inarc ]]; then
2025-03-04T20:03:39.1791735Z     echo "ARC Runner, no info on ec2 metadata"
2025-03-04T20:03:39.1792157Z   elif [[ $runner_name_str == *"gcp"* ]]; then
2025-03-04T20:03:39.1792665Z     echo "Runner is from Google Cloud Platform, No info on ec2 metadata"
2025-03-04T20:03:39.1793130Z   else
2025-03-04T20:03:39.1794032Z     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}"
2025-03-04T20:03:39.1795140Z   fi
2025-03-04T20:03:39.1795384Z }
2025-03-04T20:03:39.1795677Z echo "ami-id: $(get_ec2_metadata ami-id)"
2025-03-04T20:03:39.1796135Z echo "instance-id: $(get_ec2_metadata instance-id)"
2025-03-04T20:03:39.1796637Z echo "instance-type: $(get_ec2_metadata instance-type)"
2025-03-04T20:03:39.1797086Z echo "system info $(uname -a)"
2025-03-04T20:03:39.1803610Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:39.1804049Z env:
2025-03-04T20:03:39.1804298Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:39.1804575Z ##[endgroup]
2025-03-04T20:03:39.1942406Z ami-id: ami-05b10e08d247fb927
2025-03-04T20:03:39.2030226Z instance-id: i-0f1aad72ac3d41ea9
2025-03-04T20:03:39.2116405Z instance-type: c5.2xlarge
2025-03-04T20:03:39.2126319Z system info Linux ip-10-1-71-161.ec2.internal 6.1.128-136.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Feb 10 16:18:01 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux
2025-03-04T20:03:39.2154018Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T20:03:39.2155015Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T20:03:39.2160920Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:39.2161351Z env:
2025-03-04T20:03:39.2161597Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:39.2161884Z ##[endgroup]
2025-03-04T20:03:39.2215648Z ##[group]Run if systemctl is-active --quiet docker; then
2025-03-04T20:03:39.2216139Z if systemctl is-active --quiet docker; then
2025-03-04T20:03:39.2216571Z     echo "Docker daemon is running...";
2025-03-04T20:03:39.2216930Z else
2025-03-04T20:03:39.2217320Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2025-03-04T20:03:39.2217861Z fi
2025-03-04T20:03:39.2223156Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:39.2223572Z env:
2025-03-04T20:03:39.2223816Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:39.2224107Z ##[endgroup]
2025-03-04T20:03:39.2294193Z Docker daemon is running...
2025-03-04T20:03:39.2338518Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-04T20:03:39.2338849Z with:
2025-03-04T20:03:39.2339143Z   shell: bash
2025-03-04T20:03:39.2339634Z   timeout_minutes: 5
2025-03-04T20:03:39.2339950Z   max_attempts: 3
2025-03-04T20:03:39.2340215Z   retry_wait_seconds: 30
2025-03-04T20:03:39.2342607Z   command: AWS_ACCOUNT_ID=$(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

2025-03-04T20:03:39.2345046Z   polling_interval_seconds: 1
2025-03-04T20:03:39.2345368Z   warning_on_retry: true
2025-03-04T20:03:39.2345664Z   continue_on_error: false
2025-03-04T20:03:39.2345935Z env:
2025-03-04T20:03:39.2346184Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:39.2346477Z   AWS_RETRY_MODE: standard
2025-03-04T20:03:39.2346768Z   AWS_MAX_ATTEMPTS: 5
2025-03-04T20:03:39.2347053Z   AWS_DEFAULT_REGION: us-east-1
2025-03-04T20:03:39.2347360Z ##[endgroup]
2025-03-04T20:03:40.4760570Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T20:03:40.4761234Z Configure a credential helper to remove this warning. See
2025-03-04T20:03:40.4761856Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T20:03:40.4762268Z 
2025-03-04T20:03:40.4762398Z Login Succeeded
2025-03-04T20:03:40.9932044Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T20:03:40.9932913Z Configure a credential helper to remove this warning. See
2025-03-04T20:03:40.9933794Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T20:03:40.9934411Z 
2025-03-04T20:03:40.9934623Z Login Succeeded
2025-03-04T20:03:41.3187750Z Command completed after 1 attempt(s).
2025-03-04T20:03:41.3282175Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T20:03:41.3282759Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T20:03:41.3283254Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T20:03:41.3289812Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:41.3290224Z env:
2025-03-04T20:03:41.3290470Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:41.3290762Z ##[endgroup]
2025-03-04T20:03:41.3383131Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T20:03:41.3383776Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T20:03:41.3384239Z # shellcheck disable=SC2046
2025-03-04T20:03:41.3384610Z docker stop $(docker ps -q) || true
2025-03-04T20:03:41.3384991Z # Prune all of the docker images
2025-03-04T20:03:41.3385368Z docker system prune -af
2025-03-04T20:03:41.3390887Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:41.3391298Z env:
2025-03-04T20:03:41.3391545Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:41.3391835Z ##[endgroup]
2025-03-04T20:03:41.3630364Z "docker stop" requires at least 1 argument.
2025-03-04T20:03:41.3631092Z See 'docker stop --help'.
2025-03-04T20:03:41.3631455Z 
2025-03-04T20:03:41.3631771Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2025-03-04T20:03:41.3632331Z 
2025-03-04T20:03:41.3632552Z Stop one or more running containers
2025-03-04T20:03:41.3786485Z Total reclaimed space: 0B
2025-03-04T20:03:41.3825601Z ##[group]Run set +e
2025-03-04T20:03:41.3825933Z set +e
2025-03-04T20:03:41.3826194Z set -x
2025-03-04T20:03:41.3826448Z 
2025-03-04T20:03:41.3826722Z PT_DOMAIN=download.pytorch.org
2025-03-04T20:03:41.3827346Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
2025-03-04T20:03:41.3828361Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
2025-03-04T20:03:41.3828939Z # one is returned at random
2025-03-04T20:03:41.3829373Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
2025-03-04T20:03:41.3829785Z 
2025-03-04T20:03:41.3830046Z if [ -z "${RESOLVED_IP}" ]; then
2025-03-04T20:03:41.3830514Z   echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
2025-03-04T20:03:41.3831085Z   RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
2025-03-04T20:03:41.3831526Z 
2025-03-04T20:03:41.3831791Z   if [ -z "${RESOLVED_IP}" ]; then
2025-03-04T20:03:41.3832211Z     echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
2025-03-04T20:03:41.3832609Z     exit 1
2025-03-04T20:03:41.3832871Z   fi
2025-03-04T20:03:41.3833118Z fi
2025-03-04T20:03:41.3833352Z 
2025-03-04T20:03:41.3833647Z if grep -r "${PT_DOMAIN}" /etc/hosts; then
2025-03-04T20:03:41.3834032Z   # Clean up any old records first
2025-03-04T20:03:41.3834427Z   sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
2025-03-04T20:03:41.3834782Z fi
2025-03-04T20:03:41.3835016Z 
2025-03-04T20:03:41.3835356Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
2025-03-04T20:03:41.3835788Z cat /etc/hosts
2025-03-04T20:03:41.3841535Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:41.3841947Z env:
2025-03-04T20:03:41.3842338Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:41.3842630Z ##[endgroup]
2025-03-04T20:03:41.3865627Z + PT_DOMAIN=download.pytorch.org
2025-03-04T20:03:41.3870988Z ++ dig -4 +short download.pytorch.org
2025-03-04T20:03:41.3871731Z ++ tail -n1
2025-03-04T20:03:41.4307351Z + RESOLVED_IP=18.160.10.76
2025-03-04T20:03:41.4307787Z + '[' -z 18.160.10.76 ']'
2025-03-04T20:03:41.4308132Z + grep -r download.pytorch.org /etc/hosts
2025-03-04T20:03:41.4321372Z + echo '18.160.10.76 download.pytorch.org'
2025-03-04T20:03:41.4322162Z + sudo tee -a /etc/hosts
2025-03-04T20:03:41.7192891Z 18.160.10.76 download.pytorch.org
2025-03-04T20:03:41.7207840Z + cat /etc/hosts
2025-03-04T20:03:41.7215704Z 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
2025-03-04T20:03:41.7221378Z ::1         localhost6 localhost6.localdomain6
2025-03-04T20:03:41.7221802Z 18.160.10.76 download.pytorch.org
2025-03-04T20:03:41.7389010Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2025-03-04T20:03:41.7389530Z with:
2025-03-04T20:03:41.7390247Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7391062Z   docker-build-dir: .ci/docker
2025-03-04T20:03:41.7391377Z   working-directory: .
2025-03-04T20:03:41.7391752Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:41.7392181Z   force-push: false
2025-03-04T20:03:41.7392438Z env:
2025-03-04T20:03:41.7392675Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:41.7392950Z ##[endgroup]
2025-03-04T20:03:41.7421800Z ##[group]Run set -ex
2025-03-04T20:03:41.7422156Z set -ex
2025-03-04T20:03:41.7422402Z 
2025-03-04T20:03:41.7422832Z # If the docker build directory or the build script doesn't exist, the action will
2025-03-04T20:03:41.7423617Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2025-03-04T20:03:41.7424225Z # job could then download the pre-built image as usual
2025-03-04T20:03:41.7424789Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2025-03-04T20:03:41.7425294Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7425776Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7426216Z 
2025-03-04T20:03:41.7426601Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2025-03-04T20:03:41.7427073Z   exit 0
2025-03-04T20:03:41.7427325Z else
2025-03-04T20:03:41.7427617Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7427974Z fi
2025-03-04T20:03:41.7428199Z 
2025-03-04T20:03:41.7428568Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2025-03-04T20:03:41.7429215Z   # The docker image name already includes the ECR prefix and tag, so we can just
2025-03-04T20:03:41.7429789Z   # use it as it is, but first let's extract the tag
2025-03-04T20:03:41.7430301Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2025-03-04T20:03:41.7430844Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7431363Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7431799Z else
2025-03-04T20:03:41.7432142Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2025-03-04T20:03:41.7432634Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7433311Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7433905Z fi
2025-03-04T20:03:41.7442442Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:41.7442856Z env:
2025-03-04T20:03:41.7443100Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:41.7443537Z   REPO_NAME: pytorch
2025-03-04T20:03:41.7444278Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7445078Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-04T20:03:41.7445476Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:41.7445893Z ##[endgroup]
2025-03-04T20:03:41.7469957Z + [[ ! -d .ci/docker ]]
2025-03-04T20:03:41.7470262Z + [[ ! -f .ci/docker/build.sh ]]
2025-03-04T20:03:41.7470578Z + echo skip=false
2025-03-04T20:03:41.7471588Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e == *\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* ]]
2025-03-04T20:03:41.7479949Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7480904Z ++ awk -F '[:,]' '{print $2}'
2025-03-04T20:03:41.7502038Z + DOCKER_TAG=e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7502547Z + echo docker-tag=e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7503434Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7539863Z ##[group]Run set +e
2025-03-04T20:03:41.7540221Z set +e
2025-03-04T20:03:41.7540477Z set -x
2025-03-04T20:03:41.7540727Z 
2025-03-04T20:03:41.7540963Z login() {
2025-03-04T20:03:41.7541468Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-04T20:03:41.7542025Z }
2025-03-04T20:03:41.7542263Z 
2025-03-04T20:03:41.7542499Z retry () {
2025-03-04T20:03:41.7542818Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-04T20:03:41.7543161Z }
2025-03-04T20:03:41.7543402Z 
2025-03-04T20:03:41.7543651Z retry login "${DOCKER_REGISTRY}"
2025-03-04T20:03:41.7543988Z 
2025-03-04T20:03:41.7544234Z START_TIME=$(date +%s)
2025-03-04T20:03:41.7544560Z # Wait up to 120 minutes
2025-03-04T20:03:41.7544952Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do
2025-03-04T20:03:41.7545477Z   # Check if image already exists, if it does then skip building it
2025-03-04T20:03:41.7546004Z   if docker manifest inspect "${DOCKER_IMAGE}"; then
2025-03-04T20:03:41.7546397Z     exit 0
2025-03-04T20:03:41.7546656Z   fi
2025-03-04T20:03:41.7546897Z 
2025-03-04T20:03:41.7547314Z   # NB: This flag is used by Docker build workflow to push the image to ECR, so we can
2025-03-04T20:03:41.7548024Z   # use this to differentiate between the Docker build and regular build jobs. For the
2025-03-04T20:03:41.7548730Z   # latter, it will wait for the Docker images to become available before continuing
2025-03-04T20:03:41.7549291Z   if [ "${DOCKER_PUSH:-false}" == "true" ]; then
2025-03-04T20:03:41.7549725Z     # It's a Docker build job, let's build the image
2025-03-04T20:03:41.7550100Z     break
2025-03-04T20:03:41.7550358Z   else
2025-03-04T20:03:41.7550730Z     # It's a regular build job, wait for the image to become available
2025-03-04T20:03:41.7551176Z     sleep 300
2025-03-04T20:03:41.7551448Z   fi
2025-03-04T20:03:41.7551690Z done
2025-03-04T20:03:41.7551929Z 
2025-03-04T20:03:41.7552308Z # NB: This part requires a full checkout. Otherwise, the merge base will
2025-03-04T20:03:41.7552917Z # be empty.  The default action would be to continue rebuild the image
2025-03-04T20:03:41.7553461Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2025-03-04T20:03:41.7553945Z   # if we're on the base branch then use the parent commit
2025-03-04T20:03:41.7554532Z   MERGE_BASE=$(git rev-parse HEAD~)
2025-03-04T20:03:41.7554882Z else
2025-03-04T20:03:41.7555239Z   # otherwise we're on a PR, so use the most recent base commit
2025-03-04T20:03:41.7555756Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2025-03-04T20:03:41.7556150Z fi
2025-03-04T20:03:41.7556385Z 
2025-03-04T20:03:41.7556645Z if [[ -z "${MERGE_BASE}" ]]; then
2025-03-04T20:03:41.7557032Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7557390Z 
2025-03-04T20:03:41.7557889Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2025-03-04T20:03:41.7558474Z   exit 0
2025-03-04T20:03:41.7558726Z fi
2025-03-04T20:03:41.7559037Z 
2025-03-04T20:03:41.7559376Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2025-03-04T20:03:41.7560099Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2025-03-04T20:03:41.7560724Z   exit 1
2025-03-04T20:03:41.7560981Z fi
2025-03-04T20:03:41.7561213Z 
2025-03-04T20:03:41.7561619Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2025-03-04T20:03:41.7562321Z # If no image exists but the hash is the same as the previous hash then we should error out here
2025-03-04T20:03:41.7562956Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2025-03-04T20:03:41.7563683Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2025-03-04T20:03:41.7564501Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2025-03-04T20:03:41.7564995Z fi
2025-03-04T20:03:41.7565239Z 
2025-03-04T20:03:41.7565535Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-04T20:03:41.7571103Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:41.7571513Z env:
2025-03-04T20:03:41.7571747Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:41.7572063Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-04T20:03:41.7572437Z   BASE_REVISION: 6130f46efa5539abfeee284d298e5696e18b0475
2025-03-04T20:03:41.7573279Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7574367Z   DOCKER_TAG: e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:41.7574851Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:41.7575277Z   DOCKER_PUSH: 
2025-03-04T20:03:41.7575532Z ##[endgroup]
2025-03-04T20:03:41.7598121Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:41.7598596Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:41.7601266Z + aws ecr get-login-password --region us-east-1
2025-03-04T20:03:41.7602163Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:42.2834363Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T20:03:42.2835035Z Configure a credential helper to remove this warning. See
2025-03-04T20:03:42.2835817Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T20:03:42.2836327Z 
2025-03-04T20:03:42.2836439Z Login Succeeded
2025-03-04T20:03:42.2847849Z ++ date +%s
2025-03-04T20:03:42.2856211Z + START_TIME=1741118622
2025-03-04T20:03:42.2859786Z ++ date +%s
2025-03-04T20:03:42.2867811Z + [[ 1741111422 -lt 1741118622 ]]
2025-03-04T20:03:42.2869267Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:42.4953838Z {
2025-03-04T20:03:42.4954299Z 	"schemaVersion": 2,
2025-03-04T20:03:42.4955137Z 	"mediaType": "application/vnd.docker.distribution.manifest.v2+json",
2025-03-04T20:03:42.4955800Z 	"config": {
2025-03-04T20:03:42.4956229Z 		"mediaType": "application/vnd.docker.container.image.v1+json",
2025-03-04T20:03:42.4956938Z 		"size": 41574,
2025-03-04T20:03:42.4957629Z 		"digest": "sha256:42a061d7be1937cbc1fbc27aeded7066aaf36ff79fc6aa23ac1b24577092b231"
2025-03-04T20:03:42.4958415Z 	},
2025-03-04T20:03:42.4958684Z 	"layers": [
2025-03-04T20:03:42.4959005Z 		{
2025-03-04T20:03:42.4959637Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4960409Z 			"size": 28583948,
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2025-03-04T20:03:42.4962036Z 		},
2025-03-04T20:03:42.4962262Z 		{
2025-03-04T20:03:42.4962832Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4963290Z 			"size": 1894,
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2025-03-04T20:03:42.4964272Z 		},
2025-03-04T20:03:42.4964490Z 		{
2025-03-04T20:03:42.4964847Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4965300Z 			"size": 319462812,
2025-03-04T20:03:42.4965751Z 			"digest": "sha256:9ec3a7d783a570db730865e2744f26953d83bcd95e374e6013b1d62459345f04"
2025-03-04T20:03:42.4966251Z 		},
2025-03-04T20:03:42.4966470Z 		{
2025-03-04T20:03:42.4966813Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4967260Z 			"size": 864,
2025-03-04T20:03:42.4967697Z 			"digest": "sha256:1d45db544f5fa7596ac970117255495efbdc24b4e8e270335749eb4b91b6f4b7"
2025-03-04T20:03:42.4968202Z 		},
2025-03-04T20:03:42.4968419Z 		{
2025-03-04T20:03:42.4968774Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4969228Z 			"size": 79404693,
2025-03-04T20:03:42.4969677Z 			"digest": "sha256:bdba156b66002d5721a2dc2ae57b19485b717065f4c41347ba152f6ccfad515f"
2025-03-04T20:03:42.4970180Z 		},
2025-03-04T20:03:42.4970445Z 		{
2025-03-04T20:03:42.4970799Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4971242Z 			"size": 703,
2025-03-04T20:03:42.4971681Z 			"digest": "sha256:64fa445fb05601b1c8a6857fe696aebe3e3f32064db7f26162594dade18ed7cd"
2025-03-04T20:03:42.4972184Z 		},
2025-03-04T20:03:42.4972401Z 		{
2025-03-04T20:03:42.4972753Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4973192Z 			"size": 1257,
2025-03-04T20:03:42.4973845Z 			"digest": "sha256:ada101552cefb9a22eae905b16d457302a2eed87e1b25a19861885b93801da84"
2025-03-04T20:03:42.4974459Z 		},
2025-03-04T20:03:42.4974854Z 		{
2025-03-04T20:03:42.4975377Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4975984Z 			"size": 484,
2025-03-04T20:03:42.4976720Z 			"digest": "sha256:ef89edaafa271f35319908c25357ce10236539ea1af10d8f671abdba8335f421"
2025-03-04T20:03:42.4977406Z 		},
2025-03-04T20:03:42.4977622Z 		{
2025-03-04T20:03:42.4978065Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4978522Z 			"size": 108,
2025-03-04T20:03:42.4979041Z 			"digest": "sha256:74fbdcbcfc6926303525c5d556a1ceab706354aef4cda5e08b88d8104357d33b"
2025-03-04T20:03:42.4979929Z 		},
2025-03-04T20:03:42.4980156Z 		{
2025-03-04T20:03:42.4980511Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4980959Z 			"size": 4178,
2025-03-04T20:03:42.4981390Z 			"digest": "sha256:5a9790d486c4fd28a5a5c944a4a6c9379863b349cb5453fe65ecf2c2587aefa6"
2025-03-04T20:03:42.4981893Z 		},
2025-03-04T20:03:42.4982117Z 		{
2025-03-04T20:03:42.4982473Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4982954Z 			"size": 1859,
2025-03-04T20:03:42.4983404Z 			"digest": "sha256:92595c6dfc38d4a3e321c52fd51af0a471c01595d2cba185591e77376568e5c3"
2025-03-04T20:03:42.4983912Z 		},
2025-03-04T20:03:42.4984303Z 		{
2025-03-04T20:03:42.4984668Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4985123Z 			"size": 702,
2025-03-04T20:03:42.4985574Z 			"digest": "sha256:bd3d4c9c8676b50cb0a2a40f5cafbbb1084bdb45c44326894ea2d894460289cd"
2025-03-04T20:03:42.4986088Z 		},
2025-03-04T20:03:42.4986310Z 		{
2025-03-04T20:03:42.4986665Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4987121Z 			"size": 478,
2025-03-04T20:03:42.4987570Z 			"digest": "sha256:ce5958ac9c8ab7c749adca4a7281b9014ed3616e7970eebf23b2ba55a75f9d77"
2025-03-04T20:03:42.4988081Z 		},
2025-03-04T20:03:42.4988289Z 		{
2025-03-04T20:03:42.4988646Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4989205Z + exit 0
2025-03-04T20:03:42.4989438Z 			"size": 2820756718,
2025-03-04T20:03:42.4990009Z 			"digest": "sha256:69b5de1c7ab95f1ef612a868d98a50da57419d93d5eae202aeddf48e3e58868a"
2025-03-04T20:03:42.4990526Z 		},
2025-03-04T20:03:42.4990775Z 		{
2025-03-04T20:03:42.4991138Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4991588Z 			"size": 32,
2025-03-04T20:03:42.4992034Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.4992549Z 		},
2025-03-04T20:03:42.4992770Z 		{
2025-03-04T20:03:42.4993128Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4993586Z 			"size": 380,
2025-03-04T20:03:42.4994032Z 			"digest": "sha256:78aad6cd140aff1e67ff8c18727647690d6ea601c9a5c9fef6b0eba1d419b663"
2025-03-04T20:03:42.4994552Z 		},
2025-03-04T20:03:42.4994779Z 		{
2025-03-04T20:03:42.4995145Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4995607Z 			"size": 104,
2025-03-04T20:03:42.4996062Z 			"digest": "sha256:2e300d9764136cc288ccbed03a475ec18bfb612a436420dfeb79fa0f2af917e6"
2025-03-04T20:03:42.4996571Z 		},
2025-03-04T20:03:42.4996788Z 		{
2025-03-04T20:03:42.4997142Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4997592Z 			"size": 232,
2025-03-04T20:03:42.4998025Z 			"digest": "sha256:902b8ba7444384354d1bd737a97bb4b125fcf16026675d7a30fc6af3a33ad00a"
2025-03-04T20:03:42.4998524Z 		},
2025-03-04T20:03:42.4998740Z 		{
2025-03-04T20:03:42.4999095Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.4999541Z 			"size": 3907449,
2025-03-04T20:03:42.4999988Z 			"digest": "sha256:5bac54b04ba4f5e3de9876c85a8441de93752cb7485722df1e75d0b9c166902d"
2025-03-04T20:03:42.5000478Z 		},
2025-03-04T20:03:42.5000696Z 		{
2025-03-04T20:03:42.5001049Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5001528Z 			"size": 1946,
2025-03-04T20:03:42.5001965Z 			"digest": "sha256:f8efca90764d791057932eabc95f108075629e5b27ccd7ac1d16e292179160a1"
2025-03-04T20:03:42.5002467Z 		},
2025-03-04T20:03:42.5002689Z 		{
2025-03-04T20:03:42.5003043Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5003498Z 			"size": 105,
2025-03-04T20:03:42.5003942Z 			"digest": "sha256:f8e9163c7282fc9d8ce660b3d4d1ea59d6cb331337ec35f691f5e95c30f86285"
2025-03-04T20:03:42.5004453Z 		},
2025-03-04T20:03:42.5004678Z 		{
2025-03-04T20:03:42.5005033Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5005479Z 			"size": 802,
2025-03-04T20:03:42.5005923Z 			"digest": "sha256:865f1fbd35fba72c68578db94e85d884841b2d49da7003cf8d42e6177f58fddc"
2025-03-04T20:03:42.5006430Z 		},
2025-03-04T20:03:42.5006637Z 		{
2025-03-04T20:03:42.5006991Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5007439Z 			"size": 32,
2025-03-04T20:03:42.5007881Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.5008394Z 		},
2025-03-04T20:03:42.5008618Z 		{
2025-03-04T20:03:42.5008979Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5009563Z 			"size": 104,
2025-03-04T20:03:42.5010010Z 			"digest": "sha256:b352850bb2d4b8a8aa2495bea0246ea2e18ab87257c2db5774c22203fd8fb1a1"
2025-03-04T20:03:42.5010525Z 		},
2025-03-04T20:03:42.5010748Z 		{
2025-03-04T20:03:42.5011108Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5011558Z 			"size": 504,
2025-03-04T20:03:42.5011999Z 			"digest": "sha256:a87953804394c6e70a8eb5704868ffa2e69c2cb9c300d58bec1c1bca922ed322"
2025-03-04T20:03:42.5012504Z 		},
2025-03-04T20:03:42.5012723Z 		{
2025-03-04T20:03:42.5013064Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5013512Z 			"size": 121478537,
2025-03-04T20:03:42.5014432Z 			"digest": "sha256:ed2e9b6e0195600e61ae1fd5916bc9261240ee081afd33d7f1de400b6516864d"
2025-03-04T20:03:42.5015089Z 		},
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2025-03-04T20:03:42.5121658Z 			"size": 702,
2025-03-04T20:03:42.5122095Z 			"digest": "sha256:bd3d4c9c8676b50cb0a2a40f5cafbbb1084bdb45c44326894ea2d894460289cd"
2025-03-04T20:03:42.5122594Z 		},
2025-03-04T20:03:42.5122806Z 		{
2025-03-04T20:03:42.5123146Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5123580Z 			"size": 135,
2025-03-04T20:03:42.5124006Z 			"digest": "sha256:f54115336514320fe09d47bcb20c33d6058ff7d64f9e0e01301477ebbf984ca3"
2025-03-04T20:03:42.5124496Z 		},
2025-03-04T20:03:42.5124700Z 		{
2025-03-04T20:03:42.5125043Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5125480Z 			"size": 32,
2025-03-04T20:03:42.5125908Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.5126409Z 		},
2025-03-04T20:03:42.5126619Z 		{
2025-03-04T20:03:42.5126967Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5127406Z 			"size": 156,
2025-03-04T20:03:42.5127825Z 			"digest": "sha256:291e406a9f3d97673cc67af34c5c33971df1d8545b4f58a1e1b22f58a4d0b890"
2025-03-04T20:03:42.5128417Z 		},
2025-03-04T20:03:42.5128628Z 		{
2025-03-04T20:03:42.5128977Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5129418Z 			"size": 1483,
2025-03-04T20:03:42.5129854Z 			"digest": "sha256:d3b3ade5cbe958381e3b731a2b4f1156ede6602a7197b9033c89af8a63d80538"
2025-03-04T20:03:42.5130358Z 		},
2025-03-04T20:03:42.5130567Z 		{
2025-03-04T20:03:42.5130907Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5131343Z 			"size": 32,
2025-03-04T20:03:42.5131772Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.5132312Z 		},
2025-03-04T20:03:42.5132515Z 		{
2025-03-04T20:03:42.5132855Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5133370Z 			"size": 135,
2025-03-04T20:03:42.5133792Z 			"digest": "sha256:d82725f308d0e5ca1d792055e6ef34a0177ba61b90d7737850c5c9b76b199556"
2025-03-04T20:03:42.5134288Z 		},
2025-03-04T20:03:42.5134488Z 		{
2025-03-04T20:03:42.5134826Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5135263Z 			"size": 381,
2025-03-04T20:03:42.5135682Z 			"digest": "sha256:90430ef57b112d9c6ba7f5c07d4823adde9b25885442e4583318c4032d9888fe"
2025-03-04T20:03:42.5136171Z 		},
2025-03-04T20:03:42.5136382Z 		{
2025-03-04T20:03:42.5136727Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5137161Z 			"size": 32,
2025-03-04T20:03:42.5137589Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.5138172Z 		},
2025-03-04T20:03:42.5138378Z 		{
2025-03-04T20:03:42.5138724Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5139160Z 			"size": 102,
2025-03-04T20:03:42.5139590Z 			"digest": "sha256:b7f356b3c497cfdf496a20298b84aba9e233c683d34e19073366296ee467cb08"
2025-03-04T20:03:42.5140080Z 		},
2025-03-04T20:03:42.5140276Z 		{
2025-03-04T20:03:42.5140618Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5141053Z 			"size": 1899,
2025-03-04T20:03:42.5141481Z 			"digest": "sha256:0fe4d9a0894080cc9e761404555d51fbfc64d21cc7e614653c661ebbee0c5e35"
2025-03-04T20:03:42.5141974Z 		},
2025-03-04T20:03:42.5142187Z 		{
2025-03-04T20:03:42.5142530Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5142968Z 			"size": 234604349,
2025-03-04T20:03:42.5143433Z 			"digest": "sha256:4966abfd9eec3b22db5abcd59a276dcbeac4fe8fc5fa0c19f1a024d6ec32083d"
2025-03-04T20:03:42.5143946Z 		},
2025-03-04T20:03:42.5144156Z 		{
2025-03-04T20:03:42.5144504Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5144943Z 			"size": 106,
2025-03-04T20:03:42.5145376Z 			"digest": "sha256:ea427881fe4b2c8374b8526fb042f92c40b1071b83a854e1e4553a6c8bcb026e"
2025-03-04T20:03:42.5145871Z 		},
2025-03-04T20:03:42.5146084Z 		{
2025-03-04T20:03:42.5146429Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5146858Z 			"size": 165,
2025-03-04T20:03:42.5147282Z 			"digest": "sha256:0572404a322ac60e7a3ee66d954a8a9050d7e3154748f1694aea0f715a3828de"
2025-03-04T20:03:42.5147773Z 		},
2025-03-04T20:03:42.5147980Z 		{
2025-03-04T20:03:42.5148324Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5148760Z 			"size": 7943,
2025-03-04T20:03:42.5149191Z 			"digest": "sha256:36c75a07968b4c34f0c2847c5b926d1fec83789234dcdb44c424b5bdb9b93df1"
2025-03-04T20:03:42.5149683Z 		},
2025-03-04T20:03:42.5149892Z 		{
2025-03-04T20:03:42.5150240Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5150674Z 			"size": 8069,
2025-03-04T20:03:42.5151111Z 			"digest": "sha256:1c082ef940a4868f8b55efe7060dcadf1545207466d6ee23be215b0b5aabbfa1"
2025-03-04T20:03:42.5151618Z 		},
2025-03-04T20:03:42.5151835Z 		{
2025-03-04T20:03:42.5152188Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5152716Z 			"size": 303,
2025-03-04T20:03:42.5153142Z 			"digest": "sha256:6c30ab944f574dc1209c8f862a2f004da62bc4bc885a6326ae77c65ed22feb19"
2025-03-04T20:03:42.5153645Z 		},
2025-03-04T20:03:42.5153866Z 		{
2025-03-04T20:03:42.5154218Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5154666Z 			"size": 32,
2025-03-04T20:03:42.5155107Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.5155616Z 		},
2025-03-04T20:03:42.5155837Z 		{
2025-03-04T20:03:42.5156190Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5156636Z 			"size": 108,
2025-03-04T20:03:42.5157078Z 			"digest": "sha256:8b58f6c64c148dab51c4bb84644a2ea6ed48a24ae8b85401b526c563ed535d0a"
2025-03-04T20:03:42.5157582Z 		},
2025-03-04T20:03:42.5157859Z 		{
2025-03-04T20:03:42.5158213Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5158667Z 			"size": 54145664,
2025-03-04T20:03:42.5159117Z 			"digest": "sha256:7141d1ee4f92612b3e58371bb1d74bd296fcf73f1506c217e5a4db5ff8b64cf5"
2025-03-04T20:03:42.5159624Z 		},
2025-03-04T20:03:42.5159833Z 		{
2025-03-04T20:03:42.5160191Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T20:03:42.5160639Z 			"size": 32,
2025-03-04T20:03:42.5161076Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T20:03:42.5161582Z 		}
2025-03-04T20:03:42.5161801Z 	]
2025-03-04T20:03:42.5162015Z }
2025-03-04T20:03:42.5200883Z ##[group]Run set -eux
2025-03-04T20:03:42.5201189Z set -eux
2025-03-04T20:03:42.5202129Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin
2025-03-04T20:03:42.5209511Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:42.5209936Z env:
2025-03-04T20:03:42.5210253Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:42.5210544Z ##[endgroup]
2025-03-04T20:03:42.5237279Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token
2025-03-04T20:03:42.5238186Z + jq --raw-output .SecretString
2025-03-04T20:03:42.5239155Z + jq -r .docker_hub_readonly_token
2025-03-04T20:03:42.5240584Z + docker login --username pytorchbot --password-stdin
2025-03-04T20:03:43.0907015Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T20:03:43.0907683Z Configure a credential helper to remove this warning. See
2025-03-04T20:03:43.0908307Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T20:03:43.0908857Z 
2025-03-04T20:03:43.0909003Z Login Succeeded
2025-03-04T20:03:43.0998166Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/}
2025-03-04T20:03:43.0998574Z tag=${ECR_DOCKER_IMAGE##*/}
2025-03-04T20:03:43.0999015Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
2025-03-04T20:03:43.1004760Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:43.1005159Z env:
2025-03-04T20:03:43.1005411Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:43.1006167Z   ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.1006948Z ##[endgroup]
2025-03-04T20:03:43.1031762Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.13-clang10-e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.1086782Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2025-03-04T20:03:43.1087259Z with:
2025-03-04T20:03:43.1087947Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.1088823Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:43.1089241Z env:
2025-03-04T20:03:43.1089608Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:43.1089899Z ##[endgroup]
2025-03-04T20:03:43.1115950Z ##[group]Run set -x
2025-03-04T20:03:43.1116250Z set -x
2025-03-04T20:03:43.1116508Z set +e
2025-03-04T20:03:43.1116758Z 
2025-03-04T20:03:43.1116994Z login() {
2025-03-04T20:03:43.1117503Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-04T20:03:43.1118059Z }
2025-03-04T20:03:43.1118292Z 
2025-03-04T20:03:43.1118580Z retry () {
2025-03-04T20:03:43.1118887Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-04T20:03:43.1119233Z }
2025-03-04T20:03:43.1119468Z 
2025-03-04T20:03:43.1119732Z retry login "${DOCKER_REGISTRY}"
2025-03-04T20:03:43.1120071Z 
2025-03-04T20:03:43.1120304Z set -e
2025-03-04T20:03:43.1120669Z # ignore output since only exit code is used for conditional
2025-03-04T20:03:43.1121206Z # only pull docker image if it's not available locally
2025-03-04T20:03:43.1121789Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2025-03-04T20:03:43.1122325Z   retry docker pull "${DOCKER_IMAGE}"
2025-03-04T20:03:43.1122686Z fi
2025-03-04T20:03:43.1128059Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:03:43.1128476Z env:
2025-03-04T20:03:43.1128709Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:03:43.1129467Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.1130343Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:43.1130772Z ##[endgroup]
2025-03-04T20:03:43.1151987Z + set +e
2025-03-04T20:03:43.1152549Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:43.1155337Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:43.1156038Z + aws ecr get-login-password --region us-east-1
2025-03-04T20:03:43.1156944Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T20:03:43.6343724Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T20:03:43.6344768Z Configure a credential helper to remove this warning. See
2025-03-04T20:03:43.6345471Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T20:03:43.6346115Z 
2025-03-04T20:03:43.6351891Z Login Succeeded
2025-03-04T20:03:43.6360856Z + set -e
2025-03-04T20:03:43.6361673Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.6473978Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.9082969Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:03:43.9085009Z e4800fd93ba7d48bf4197a488fd32c12de647b0e: Pulling from pytorch/pytorch-linux-focal-py3.13-clang10
2025-03-04T20:03:43.9089138Z 86e5016c2693: Pulling fs layer
2025-03-04T20:03:43.9089759Z 093355f4c4ad: Pulling fs layer
2025-03-04T20:03:43.9090282Z 9ec3a7d783a5: Pulling fs layer
2025-03-04T20:03:43.9090771Z 1d45db544f5f: Pulling fs layer
2025-03-04T20:03:43.9091283Z bdba156b6600: Pulling fs layer
2025-03-04T20:03:43.9091923Z 64fa445fb056: Pulling fs layer
2025-03-04T20:03:43.9092328Z ada101552cef: Pulling fs layer
2025-03-04T20:03:43.9092737Z ef89edaafa27: Pulling fs layer
2025-03-04T20:03:43.9093185Z 74fbdcbcfc69: Pulling fs layer
2025-03-04T20:03:43.9093605Z 5a9790d486c4: Pulling fs layer
2025-03-04T20:03:43.9093983Z 92595c6dfc38: Pulling fs layer
2025-03-04T20:03:43.9094295Z bd3d4c9c8676: Pulling fs layer
2025-03-04T20:03:43.9094683Z ce5958ac9c8a: Pulling fs layer
2025-03-04T20:03:43.9095161Z 1d45db544f5f: Waiting
2025-03-04T20:03:43.9095509Z 69b5de1c7ab9: Pulling fs layer
2025-03-04T20:03:43.9095963Z bdba156b6600: Waiting
2025-03-04T20:03:43.9096376Z 4f4fb700ef54: Pulling fs layer
2025-03-04T20:03:43.9096752Z 78aad6cd140a: Pulling fs layer
2025-03-04T20:03:43.9112857Z 64fa445fb056: Waiting
2025-03-04T20:03:43.9113320Z 2e300d976413: Pulling fs layer
2025-03-04T20:03:43.9113770Z ada101552cef: Waiting
2025-03-04T20:03:43.9114076Z 902b8ba74443: Pulling fs layer
2025-03-04T20:03:43.9114395Z 5bac54b04ba4: Pulling fs layer
2025-03-04T20:03:43.9114734Z f8efca90764d: Pulling fs layer
2025-03-04T20:03:43.9115034Z f8e9163c7282: Pulling fs layer
2025-03-04T20:03:43.9115337Z ef89edaafa27: Waiting
2025-03-04T20:03:43.9115680Z 74fbdcbcfc69: Waiting
2025-03-04T20:03:43.9115977Z 865f1fbd35fb: Pulling fs layer
2025-03-04T20:03:43.9116319Z 5a9790d486c4: Waiting
2025-03-04T20:03:43.9116699Z 92595c6dfc38: Waiting
2025-03-04T20:03:43.9117106Z b352850bb2d4: Pulling fs layer
2025-03-04T20:03:43.9117613Z a87953804394: Pulling fs layer
2025-03-04T20:03:43.9118088Z bd3d4c9c8676: Waiting
2025-03-04T20:03:43.9118437Z ce5958ac9c8a: Waiting
2025-03-04T20:03:43.9118887Z ed2e9b6e0195: Pulling fs layer
2025-03-04T20:03:43.9119397Z 69b5de1c7ab9: Waiting
2025-03-04T20:03:43.9119829Z 4f4fb700ef54: Waiting
2025-03-04T20:03:43.9120141Z fe37bc852cfe: Pulling fs layer
2025-03-04T20:03:43.9120437Z 78aad6cd140a: Waiting
2025-03-04T20:03:43.9120836Z 5b7b22fd90a3: Pulling fs layer
2025-03-04T20:03:43.9121361Z f8efca90764d: Waiting
2025-03-04T20:03:43.9121769Z 865f1fbd35fb: Waiting
2025-03-04T20:03:43.9122037Z f8e9163c7282: Waiting
2025-03-04T20:03:43.9122351Z 3f21ba0fd88a: Pulling fs layer
2025-03-04T20:03:43.9122667Z 71532c4dfbae: Pulling fs layer
2025-03-04T20:03:43.9122977Z c7cab693dc53: Pulling fs layer
2025-03-04T20:03:43.9123272Z 2e300d976413: Waiting
2025-03-04T20:03:43.9123544Z 2c4275d1eec7: Pulling fs layer
2025-03-04T20:03:43.9123838Z a87953804394: Waiting
2025-03-04T20:03:43.9124114Z b52701aa1c2d: Pulling fs layer
2025-03-04T20:03:43.9124438Z 0fb3f36e42bc: Pulling fs layer
2025-03-04T20:03:43.9124729Z fe37bc852cfe: Waiting
2025-03-04T20:03:43.9125001Z 5b7b22fd90a3: Waiting
2025-03-04T20:03:43.9125301Z 89e1f5522c24: Pulling fs layer
2025-03-04T20:03:43.9125812Z 2e639a6c1acf: Pulling fs layer
2025-03-04T20:03:43.9126342Z 5bac54b04ba4: Waiting
2025-03-04T20:03:43.9126781Z 71532c4dfbae: Waiting
2025-03-04T20:03:43.9127222Z 76fd31917528: Pulling fs layer
2025-03-04T20:03:43.9127716Z fd69024db664: Pulling fs layer
2025-03-04T20:03:43.9128231Z b36d40c9985e: Pulling fs layer
2025-03-04T20:03:43.9128710Z 0fb3f36e42bc: Waiting
2025-03-04T20:03:43.9129132Z 3f21ba0fd88a: Waiting
2025-03-04T20:03:43.9129556Z c7cab693dc53: Waiting
2025-03-04T20:03:43.9129959Z 2c4275d1eec7: Waiting
2025-03-04T20:03:43.9130402Z 16887c8b037d: Pulling fs layer
2025-03-04T20:03:43.9130885Z ed2e9b6e0195: Waiting
2025-03-04T20:03:43.9131329Z d0f65f3b1f0d: Pulling fs layer
2025-03-04T20:03:43.9131816Z 89e1f5522c24: Waiting
2025-03-04T20:03:43.9132264Z 03802c5a71d5: Pulling fs layer
2025-03-04T20:03:43.9132759Z ccadac4a2fbe: Pulling fs layer
2025-03-04T20:03:43.9133282Z 2e639a6c1acf: Waiting
2025-03-04T20:03:43.9133743Z 76fd31917528: Waiting
2025-03-04T20:03:43.9134204Z c722505841d4: Pulling fs layer
2025-03-04T20:03:43.9134702Z b36d40c9985e: Waiting
2025-03-04T20:03:43.9135166Z b88292d4e60e: Pulling fs layer
2025-03-04T20:03:43.9135649Z 03802c5a71d5: Waiting
2025-03-04T20:03:43.9136075Z c722505841d4: Waiting
2025-03-04T20:03:43.9136525Z 8099d55a7cfc: Pulling fs layer
2025-03-04T20:03:43.9137239Z 16887c8b037d: Waiting
2025-03-04T20:03:43.9137675Z fd69024db664: Waiting
2025-03-04T20:03:43.9138202Z 79cc152bba0b: Pulling fs layer
2025-03-04T20:03:43.9138672Z 8099d55a7cfc: Waiting
2025-03-04T20:03:43.9139126Z 5f0c80af2164: Pulling fs layer
2025-03-04T20:03:43.9139549Z 426bfceca112: Pulling fs layer
2025-03-04T20:03:43.9139936Z 95c011b16a16: Pulling fs layer
2025-03-04T20:03:43.9140291Z 79cc152bba0b: Waiting
2025-03-04T20:03:43.9140685Z d75fc1c50205: Pulling fs layer
2025-03-04T20:03:43.9141140Z 95c011b16a16: Waiting
2025-03-04T20:03:43.9141419Z 5d2ee0d82c58: Pulling fs layer
2025-03-04T20:03:43.9141716Z d75fc1c50205: Waiting
2025-03-04T20:03:43.9141991Z 12099d9b52ff: Pulling fs layer
2025-03-04T20:03:43.9142288Z 5d2ee0d82c58: Waiting
2025-03-04T20:03:43.9142592Z 2b640a6b31af: Pulling fs layer
2025-03-04T20:03:43.9143083Z 0a48fbebfcf4: Pulling fs layer
2025-03-04T20:03:43.9143570Z 2b640a6b31af: Waiting
2025-03-04T20:03:43.9144022Z 0a48fbebfcf4: Waiting
2025-03-04T20:03:43.9144428Z 21ae6fb7452f: Pulling fs layer
2025-03-04T20:03:43.9144945Z 70849ecfcec9: Pulling fs layer
2025-03-04T20:03:43.9145265Z ae2290e16f54: Pulling fs layer
2025-03-04T20:03:43.9145565Z 21ae6fb7452f: Waiting
2025-03-04T20:03:43.9145835Z 70849ecfcec9: Waiting
2025-03-04T20:03:43.9146104Z ae2290e16f54: Waiting
2025-03-04T20:03:43.9146377Z f54115336514: Pulling fs layer
2025-03-04T20:03:43.9146684Z 291e406a9f3d: Pulling fs layer
2025-03-04T20:03:43.9146998Z d3b3ade5cbe9: Pulling fs layer
2025-03-04T20:03:43.9147359Z f54115336514: Waiting
2025-03-04T20:03:43.9147633Z d82725f308d0: Pulling fs layer
2025-03-04T20:03:43.9147940Z 90430ef57b11: Pulling fs layer
2025-03-04T20:03:43.9148278Z 291e406a9f3d: Waiting
2025-03-04T20:03:43.9148582Z d82725f308d0: Waiting
2025-03-04T20:03:43.9148844Z b7f356b3c497: Pulling fs layer
2025-03-04T20:03:43.9149178Z d3b3ade5cbe9: Waiting
2025-03-04T20:03:43.9149457Z 0fe4d9a08940: Pulling fs layer
2025-03-04T20:03:43.9149766Z 4966abfd9eec: Pulling fs layer
2025-03-04T20:03:43.9150079Z ea427881fe4b: Pulling fs layer
2025-03-04T20:03:43.9150381Z 90430ef57b11: Waiting
2025-03-04T20:03:43.9150644Z b7f356b3c497: Waiting
2025-03-04T20:03:43.9150915Z 0572404a322a: Pulling fs layer
2025-03-04T20:03:43.9151211Z 0fe4d9a08940: Waiting
2025-03-04T20:03:43.9151484Z 36c75a07968b: Pulling fs layer
2025-03-04T20:03:43.9151792Z 1c082ef940a4: Pulling fs layer
2025-03-04T20:03:43.9152098Z 6c30ab944f57: Pulling fs layer
2025-03-04T20:03:43.9152394Z 36c75a07968b: Waiting
2025-03-04T20:03:43.9152665Z 1c082ef940a4: Waiting
2025-03-04T20:03:43.9152939Z 8b58f6c64c14: Pulling fs layer
2025-03-04T20:03:43.9153237Z 7141d1ee4f92: Pulling fs layer
2025-03-04T20:03:43.9153533Z 7141d1ee4f92: Waiting
2025-03-04T20:03:43.9153794Z ea427881fe4b: Waiting
2025-03-04T20:03:43.9154060Z 8b58f6c64c14: Waiting
2025-03-04T20:03:43.9154321Z 6c30ab944f57: Waiting
2025-03-04T20:03:43.9839466Z 093355f4c4ad: Verifying Checksum
2025-03-04T20:03:43.9840038Z 093355f4c4ad: Download complete
2025-03-04T20:03:44.0840572Z 1d45db544f5f: Verifying Checksum
2025-03-04T20:03:44.0841240Z 1d45db544f5f: Download complete
2025-03-04T20:03:44.2692150Z 86e5016c2693: Verifying Checksum
2025-03-04T20:03:44.2692588Z 86e5016c2693: Download complete
2025-03-04T20:03:44.3709547Z 64fa445fb056: Verifying Checksum
2025-03-04T20:03:44.3710199Z 64fa445fb056: Download complete
2025-03-04T20:03:44.5003081Z ada101552cef: Verifying Checksum
2025-03-04T20:03:44.5003512Z ada101552cef: Download complete
2025-03-04T20:03:44.5702687Z ef89edaafa27: Verifying Checksum
2025-03-04T20:03:44.5703974Z ef89edaafa27: Download complete
2025-03-04T20:03:44.6521880Z 74fbdcbcfc69: Download complete
2025-03-04T20:03:44.7234623Z 5a9790d486c4: Verifying Checksum
2025-03-04T20:03:44.8021201Z 5a9790d486c4: Download complete
2025-03-04T20:03:44.8021773Z 92595c6dfc38: Verifying Checksum
2025-03-04T20:03:44.8022149Z 92595c6dfc38: Download complete
2025-03-04T20:03:44.8653091Z bd3d4c9c8676: Verifying Checksum
2025-03-04T20:03:44.8654208Z bd3d4c9c8676: Download complete
2025-03-04T20:03:44.9310004Z bdba156b6600: Verifying Checksum
2025-03-04T20:03:44.9310898Z bdba156b6600: Download complete
2025-03-04T20:03:44.9312985Z ce5958ac9c8a: Verifying Checksum
2025-03-04T20:03:44.9313483Z ce5958ac9c8a: Download complete
2025-03-04T20:03:44.9397640Z 4f4fb700ef54: Verifying Checksum
2025-03-04T20:03:45.0184467Z 78aad6cd140a: Download complete
2025-03-04T20:03:45.0884818Z 2e300d976413: Download complete
2025-03-04T20:03:45.1268042Z 86e5016c2693: Pull complete
2025-03-04T20:03:45.1479211Z 093355f4c4ad: Pull complete
2025-03-04T20:03:45.1667437Z 902b8ba74443: Verifying Checksum
2025-03-04T20:03:45.1667897Z 902b8ba74443: Download complete
2025-03-04T20:03:45.3718378Z 5bac54b04ba4: Verifying Checksum
2025-03-04T20:03:45.3719020Z 5bac54b04ba4: Download complete
2025-03-04T20:03:45.4560222Z f8efca90764d: Verifying Checksum
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2025-03-04T20:03:45.5280593Z f8e9163c7282: Verifying Checksum
2025-03-04T20:03:45.5280983Z f8e9163c7282: Download complete
2025-03-04T20:03:45.6069089Z 865f1fbd35fb: Verifying Checksum
2025-03-04T20:03:45.6069752Z 865f1fbd35fb: Download complete
2025-03-04T20:03:45.6695725Z b352850bb2d4: Verifying Checksum
2025-03-04T20:03:45.6696291Z b352850bb2d4: Download complete
2025-03-04T20:03:45.7463260Z a87953804394: Download complete
2025-03-04T20:03:47.0086179Z ed2e9b6e0195: Verifying Checksum
2025-03-04T20:03:47.0086794Z ed2e9b6e0195: Download complete
2025-03-04T20:03:47.1016398Z fe37bc852cfe: Download complete
2025-03-04T20:03:47.1597261Z 9ec3a7d783a5: Verifying Checksum
2025-03-04T20:03:47.1870892Z 9ec3a7d783a5: Download complete
2025-03-04T20:03:47.1871461Z 5b7b22fd90a3: Verifying Checksum
2025-03-04T20:03:47.1871857Z 5b7b22fd90a3: Download complete
2025-03-04T20:03:47.2287918Z 3f21ba0fd88a: Download complete
2025-03-04T20:03:47.2637186Z 71532c4dfbae: Verifying Checksum
2025-03-04T20:03:47.2880929Z 71532c4dfbae: Download complete
2025-03-04T20:03:47.2881383Z c7cab693dc53: Verifying Checksum
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2025-03-04T20:03:47.3569109Z b52701aa1c2d: Verifying Checksum
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2025-03-04T20:03:47.4465837Z 0fb3f36e42bc: Verifying Checksum
2025-03-04T20:03:47.4466441Z 0fb3f36e42bc: Download complete
2025-03-04T20:03:47.5524483Z 89e1f5522c24: Download complete
2025-03-04T20:03:47.6226030Z 2e639a6c1acf: Verifying Checksum
2025-03-04T20:03:47.6226592Z 2e639a6c1acf: Download complete
2025-03-04T20:03:47.6894313Z 76fd31917528: Verifying Checksum
2025-03-04T20:03:47.6894943Z 76fd31917528: Download complete
2025-03-04T20:03:47.7652943Z fd69024db664: Verifying Checksum
2025-03-04T20:03:47.7653429Z fd69024db664: Download complete
2025-03-04T20:03:48.2607437Z b36d40c9985e: Verifying Checksum
2025-03-04T20:03:48.2607837Z b36d40c9985e: Download complete
2025-03-04T20:03:48.3397860Z 16887c8b037d: Verifying Checksum
2025-03-04T20:03:48.3398319Z 16887c8b037d: Download complete
2025-03-04T20:03:48.5561325Z d0f65f3b1f0d: Verifying Checksum
2025-03-04T20:03:48.5562143Z d0f65f3b1f0d: Download complete
2025-03-04T20:03:48.6208372Z 03802c5a71d5: Download complete
2025-03-04T20:03:48.7072395Z ccadac4a2fbe: Download complete
2025-03-04T20:03:48.9612285Z c722505841d4: Verifying Checksum
2025-03-04T20:03:48.9612998Z c722505841d4: Download complete
2025-03-04T20:03:49.0530262Z b88292d4e60e: Verifying Checksum
2025-03-04T20:03:49.0530884Z b88292d4e60e: Download complete
2025-03-04T20:03:49.1265953Z 8099d55a7cfc: Verifying Checksum
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2025-03-04T20:03:49.2209144Z 79cc152bba0b: Verifying Checksum
2025-03-04T20:03:49.2209801Z 79cc152bba0b: Download complete
2025-03-04T20:03:49.2952423Z 5f0c80af2164: Verifying Checksum
2025-03-04T20:03:49.2953007Z 5f0c80af2164: Download complete
2025-03-04T20:03:49.3788079Z 426bfceca112: Verifying Checksum
2025-03-04T20:03:49.3788583Z 426bfceca112: Download complete
2025-03-04T20:03:49.4577432Z 95c011b16a16: Download complete
2025-03-04T20:03:49.5249369Z d75fc1c50205: Download complete
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2025-03-04T20:03:49.6773168Z 12099d9b52ff: Download complete
2025-03-04T20:03:49.7378091Z 2b640a6b31af: Verifying Checksum
2025-03-04T20:03:49.7378804Z 2b640a6b31af: Download complete
2025-03-04T20:03:49.8184035Z 0a48fbebfcf4: Verifying Checksum
2025-03-04T20:03:49.8184543Z 0a48fbebfcf4: Download complete
2025-03-04T20:03:49.8953124Z 21ae6fb7452f: Verifying Checksum
2025-03-04T20:03:49.8953700Z 21ae6fb7452f: Download complete
2025-03-04T20:03:49.9772319Z 70849ecfcec9: Verifying Checksum
2025-03-04T20:03:49.9773032Z 70849ecfcec9: Download complete
2025-03-04T20:03:50.0396226Z ae2290e16f54: Verifying Checksum
2025-03-04T20:03:50.0396705Z ae2290e16f54: Download complete
2025-03-04T20:03:50.1160856Z f54115336514: Verifying Checksum
2025-03-04T20:03:50.2058629Z f54115336514: Download complete
2025-03-04T20:03:50.2059031Z 291e406a9f3d: Verifying Checksum
2025-03-04T20:03:50.2059362Z 291e406a9f3d: Download complete
2025-03-04T20:03:50.2726976Z d3b3ade5cbe9: Verifying Checksum
2025-03-04T20:03:50.2727650Z d3b3ade5cbe9: Download complete
2025-03-04T20:03:50.3570442Z d82725f308d0: Download complete
2025-03-04T20:03:50.4262773Z 90430ef57b11: Verifying Checksum
2025-03-04T20:03:50.4264198Z 90430ef57b11: Download complete
2025-03-04T20:03:50.5103283Z b7f356b3c497: Download complete
2025-03-04T20:03:50.5677077Z 0fe4d9a08940: Verifying Checksum
2025-03-04T20:03:50.5677813Z 0fe4d9a08940: Download complete
2025-03-04T20:03:51.5845261Z 2c4275d1eec7: Verifying Checksum
2025-03-04T20:03:51.5845978Z 2c4275d1eec7: Download complete
2025-03-04T20:03:51.6702720Z ea427881fe4b: Verifying Checksum
2025-03-04T20:03:51.6704358Z ea427881fe4b: Download complete
2025-03-04T20:03:51.7480959Z 0572404a322a: Verifying Checksum
2025-03-04T20:03:51.7481540Z 0572404a322a: Download complete
2025-03-04T20:03:51.8314688Z 36c75a07968b: Download complete
2025-03-04T20:03:51.9044116Z 1c082ef940a4: Download complete
2025-03-04T20:03:52.9628927Z 4966abfd9eec: Verifying Checksum
2025-03-04T20:03:52.9629569Z 4966abfd9eec: Download complete
2025-03-04T20:03:52.9929652Z 6c30ab944f57: Verifying Checksum
2025-03-04T20:03:52.9930145Z 6c30ab944f57: Download complete
2025-03-04T20:03:53.0402668Z 8b58f6c64c14: Verifying Checksum
2025-03-04T20:03:53.0403123Z 8b58f6c64c14: Download complete
2025-03-04T20:03:53.5842578Z 7141d1ee4f92: Verifying Checksum
2025-03-04T20:03:53.5843173Z 7141d1ee4f92: Download complete
2025-03-04T20:03:55.7054045Z 9ec3a7d783a5: Pull complete
2025-03-04T20:03:55.7590708Z 1d45db544f5f: Pull complete
2025-03-04T20:03:57.7078170Z bdba156b6600: Pull complete
2025-03-04T20:03:57.8450637Z 64fa445fb056: Pull complete
2025-03-04T20:03:57.9674676Z ada101552cef: Pull complete
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2025-03-04T20:03:58.2988481Z 74fbdcbcfc69: Pull complete
2025-03-04T20:03:58.4615799Z 5a9790d486c4: Pull complete
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2025-03-04T20:03:58.7132516Z bd3d4c9c8676: Pull complete
2025-03-04T20:03:58.7620916Z ce5958ac9c8a: Pull complete
2025-03-04T20:04:13.1883625Z 69b5de1c7ab9: Download complete
2025-03-04T20:05:01.2981359Z 69b5de1c7ab9: Pull complete
2025-03-04T20:05:01.3078615Z 4f4fb700ef54: Pull complete
2025-03-04T20:05:01.3179171Z 78aad6cd140a: Pull complete
2025-03-04T20:05:01.3271744Z 2e300d976413: Pull complete
2025-03-04T20:05:01.3382914Z 902b8ba74443: Pull complete
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2025-03-04T20:05:22.8254060Z ae2290e16f54: Pull complete
2025-03-04T20:05:22.8472654Z f54115336514: Pull complete
2025-03-04T20:05:22.8660782Z 291e406a9f3d: Pull complete
2025-03-04T20:05:22.8762149Z d3b3ade5cbe9: Pull complete
2025-03-04T20:05:22.8957687Z d82725f308d0: Pull complete
2025-03-04T20:05:22.9050093Z 90430ef57b11: Pull complete
2025-03-04T20:05:22.9297553Z b7f356b3c497: Pull complete
2025-03-04T20:05:22.9512692Z 0fe4d9a08940: Pull complete
2025-03-04T20:05:28.3660181Z 4966abfd9eec: Pull complete
2025-03-04T20:05:28.3878432Z ea427881fe4b: Pull complete
2025-03-04T20:05:28.4094660Z 0572404a322a: Pull complete
2025-03-04T20:05:28.4308865Z 36c75a07968b: Pull complete
2025-03-04T20:05:28.4531979Z 1c082ef940a4: Pull complete
2025-03-04T20:05:28.4746370Z 6c30ab944f57: Pull complete
2025-03-04T20:05:28.5183554Z 8b58f6c64c14: Pull complete
2025-03-04T20:05:30.0481184Z 7141d1ee4f92: Pull complete
2025-03-04T20:05:30.0984716Z Digest: sha256:4b06140a996af551b7b0ac83f477dc941e0c9222ab7629a7b7ab8abac73604c2
2025-03-04T20:05:30.1026274Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:05:30.1056665Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:05:30.1101171Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T20:05:30.1102186Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T20:05:30.1109242Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:30.1109657Z env:
2025-03-04T20:05:30.1109901Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:30.1110190Z ##[endgroup]
2025-03-04T20:05:30.1201702Z Prepare all required actions
2025-03-04T20:05:30.1243689Z ##[group]Run ./.github/actions/get-workflow-job-id
2025-03-04T20:05:30.1244088Z with:
2025-03-04T20:05:30.1244515Z   github-token: ***
2025-03-04T20:05:30.1244774Z env:
2025-03-04T20:05:30.1245017Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:30.1245308Z ##[endgroup]
2025-03-04T20:05:30.1271291Z ##[group]Run set -eux
2025-03-04T20:05:30.1271594Z set -eux
2025-03-04T20:05:30.1272075Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-04T20:05:30.1278186Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:30.1278598Z env:
2025-03-04T20:05:30.1278828Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:30.1279300Z   GITHUB_TOKEN: ***
2025-03-04T20:05:30.1279555Z ##[endgroup]
2025-03-04T20:05:30.1301943Z + python3 .github/scripts/get_workflow_job_id.py 13661694839 i-0f1aad72ac3d41ea9
2025-03-04T20:05:31.4750330Z setting job-id=38194769830
2025-03-04T20:05:31.4751026Z setting job-name=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:05:31.4884852Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-04T20:05:31.4885774Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-04T20:05:31.4886379Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2025-03-04T20:05:31.4886885Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2025-03-04T20:05:31.4892749Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:31.4893159Z env:
2025-03-04T20:05:31.4893429Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:31.4893705Z   JOB_ID: 38194769830
2025-03-04T20:05:31.4894153Z   JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:05:31.4894668Z   WORKFLOW_NAME: pull
2025-03-04T20:05:31.4894951Z   WORKFLOW_RUN_ID: 13661694839
2025-03-04T20:05:31.4895250Z ##[endgroup]
2025-03-04T20:05:31.9354700Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T20:05:32.3110692Z Collecting psutil==5.9.1
2025-03-04T20:05:32.3340212Z   Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB)
2025-03-04T20:05:32.3810592Z Collecting nvidia-ml-py==11.525.84
2025-03-04T20:05:32.3847653Z   Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB)
2025-03-04T20:05:32.4463330Z Collecting dataclasses_json==0.6.7
2025-03-04T20:05:32.4498997Z   Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)
2025-03-04T20:05:32.4837543Z Collecting typing-inspect<1,>=0.4.0
2025-03-04T20:05:32.4870984Z   Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)
2025-03-04T20:05:32.6001134Z Collecting marshmallow<4.0.0,>=3.18.0
2025-03-04T20:05:32.6036528Z   Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB)
2025-03-04T20:05:32.6592286Z Collecting packaging>=17.0
2025-03-04T20:05:32.6626327Z   Downloading packaging-24.2-py3-none-any.whl (65 kB)
2025-03-04T20:05:32.6861707Z Collecting mypy-extensions>=0.3.0
2025-03-04T20:05:32.6898118Z   Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)
2025-03-04T20:05:32.7323525Z Collecting typing-extensions>=3.7.4
2025-03-04T20:05:32.7366158Z   Downloading typing_extensions-4.12.2-py3-none-any.whl (37 kB)
2025-03-04T20:05:32.8263125Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json
2025-03-04T20:05:33.0952062Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.0.0 nvidia-ml-py-11.525.84 packaging-24.2 psutil-5.9.1 typing-extensions-4.12.2 typing-inspect-0.9.0
2025-03-04T20:05:33.2893403Z Prepare all required actions
2025-03-04T20:05:33.2893835Z Getting action download info
2025-03-04T20:05:33.4581781Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2025-03-04T20:05:33.6490924Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806)
2025-03-04T20:05:33.9415397Z ##[group]Run ./.github/actions/download-build-artifacts
2025-03-04T20:05:33.9415799Z with:
2025-03-04T20:05:33.9416060Z   name: linux-focal-py3.13-clang10
2025-03-04T20:05:33.9416397Z   s3-bucket: gha-artifacts
2025-03-04T20:05:33.9416682Z env:
2025-03-04T20:05:33.9416921Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:33.9417205Z ##[endgroup]
2025-03-04T20:05:33.9452118Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-04T20:05:33.9452501Z with:
2025-03-04T20:05:33.9452763Z   name: linux-focal-py3.13-clang10
2025-03-04T20:05:33.9453099Z   s3-bucket: gha-artifacts
2025-03-04T20:05:33.9453436Z   region: us-east-1
2025-03-04T20:05:33.9453690Z env:
2025-03-04T20:05:33.9453924Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:33.9454210Z ##[endgroup]
2025-03-04T20:05:34.4116776Z (node:40809) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-04T20:05:34.4117659Z 
2025-03-04T20:05:34.4118043Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-04T20:05:34.4119420Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-04T20:05:34.4120453Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-04T20:05:34.6268789Z Found 1 objects with prefix pytorch/pytorch/13661694839/linux-focal-py3.13-clang10/
2025-03-04T20:05:34.6269581Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-04T20:05:38.6639897Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-04T20:05:38.6646303Z Artifact download has finished successfully
2025-03-04T20:05:38.6810927Z ##[group]Run unzip -o artifacts.zip
2025-03-04T20:05:38.6811304Z unzip -o artifacts.zip
2025-03-04T20:05:38.6817082Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:38.6817500Z env:
2025-03-04T20:05:38.6817845Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:38.6818144Z ##[endgroup]
2025-03-04T20:05:38.7028346Z Archive:  artifacts.zip
2025-03-04T20:05:38.7029427Z    creating: dist/
2025-03-04T20:05:39.6826521Z   inflating: dist/torch-2.7.0a0+git1b74980-cp313-cp313-linux_x86_64.whl  
2025-03-04T20:05:39.6827108Z    creating: build/custom_test_artifacts/
2025-03-04T20:05:39.6827540Z    creating: build/custom_test_artifacts/custom-op-build/
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2025-03-04T20:05:44.6804501Z   inflating: build/bin/cpu_generator_test  
2025-03-04T20:05:44.6866359Z   inflating: build/bin/MaybeOwned_test  
2025-03-04T20:05:44.6964992Z   inflating: build/bin/kernel_lambda_test  
2025-03-04T20:05:44.7016192Z   inflating: build/bin/test_parallel  
2025-03-04T20:05:44.7066998Z   inflating: build/bin/cpu_profiling_allocator_test  
2025-03-04T20:05:44.7117149Z   inflating: build/bin/half_test     
2025-03-04T20:05:44.7242040Z   inflating: build/bin/kernel_lambda_legacy_test  
2025-03-04T20:05:44.7290598Z   inflating: build/bin/cpu_allocator_test  
2025-03-04T20:05:44.7340409Z   inflating: build/bin/static_runtime_bench  
2025-03-04T20:05:44.7390370Z   inflating: build/bin/Dimname_test  
2025-03-04T20:05:44.7391896Z   inflating: build/bin/verify_api_visibility  
2025-03-04T20:05:44.7441777Z   inflating: build/bin/memory_overlapping_test  
2025-03-04T20:05:44.7498003Z   inflating: build/bin/atest         
2025-03-04T20:05:44.7586536Z   inflating: build/bin/cpu_rng_test  
2025-03-04T20:05:44.7634021Z   inflating: build/bin/dispatch_key_set_test  
2025-03-04T20:05:44.7915878Z   inflating: build/bin/static_runtime_test  
2025-03-04T20:05:44.7918246Z   inflating: build/bin/thread_init_test  
2025-03-04T20:05:44.7977108Z   inflating: build/bin/inline_container_test  
2025-03-04T20:05:44.8025703Z   inflating: build/bin/operators_test  
2025-03-04T20:05:44.8121611Z   inflating: build/bin/List_test     
2025-03-04T20:05:44.8169567Z   inflating: build/bin/operator_name_test  
2025-03-04T20:05:44.8218863Z   inflating: build/bin/wrapdim_test  
2025-03-04T20:05:44.8267196Z   inflating: build/bin/dlconvertor_test  
2025-03-04T20:05:44.8322747Z   inflating: build/bin/extension_backend_test  
2025-03-04T20:05:44.8372353Z   inflating: build/bin/undefined_tensor_test  
2025-03-04T20:05:44.8466093Z   inflating: build/bin/ivalue_test   
2025-03-04T20:05:44.8513936Z   inflating: build/bin/CppSignature_test  
2025-03-04T20:05:44.8561225Z   inflating: build/bin/lazy_tensor_test  
2025-03-04T20:05:44.8612167Z   inflating: build/bin/mobile_memory_cleanup  
2025-03-04T20:05:44.9866711Z   inflating: build/bin/test_api      
2025-03-04T20:05:44.9920538Z   inflating: build/bin/scalar_tensor_test  
2025-03-04T20:05:45.0226732Z   inflating: build/bin/op_registration_test  
2025-03-04T20:05:45.0277758Z   inflating: build/bin/math_kernel_test  
2025-03-04T20:05:45.0327362Z   inflating: build/bin/memory_format_test  
2025-03-04T20:05:45.0375388Z   inflating: build/bin/reduce_ops_test  
2025-03-04T20:05:45.0429171Z   inflating: build/bin/native_test   
2025-03-04T20:05:45.0478789Z   inflating: build/bin/packedtensoraccessor_test  
2025-03-04T20:05:45.0546401Z   inflating: build/bin/pow_test      
2025-03-04T20:05:45.0600063Z   inflating: build/bin/quantized_test  
2025-03-04T20:05:45.0648647Z   inflating: build/bin/reportMemoryUsage_test  
2025-03-04T20:05:45.0699282Z   inflating: build/bin/test_edge_op_registration  
2025-03-04T20:05:45.0703281Z   inflating: build/bin/torch_shm_manager  
2025-03-04T20:05:45.0718569Z   inflating: build/bin/tutorial_tensorexpr  
2025-03-04T20:05:45.1704058Z   inflating: build/bin/test_tensorexpr  
2025-03-04T20:05:45.2260889Z   inflating: build/bin/test_jit      
2025-03-04T20:05:45.2261295Z    creating: .additional_ci_files/
2025-03-04T20:05:45.2353991Z   inflating: .additional_ci_files/test-times.json  
2025-03-04T20:05:45.2720805Z   inflating: .additional_ci_files/test-class-times.json  
2025-03-04T20:05:45.2751316Z ##[group]Run rm artifacts.zip
2025-03-04T20:05:45.2751663Z rm artifacts.zip
2025-03-04T20:05:45.2757378Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:45.2757809Z env:
2025-03-04T20:05:45.2758052Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:45.2758340Z ##[endgroup]
2025-03-04T20:05:45.3189636Z ##[group]Run df -H
2025-03-04T20:05:45.3189938Z df -H
2025-03-04T20:05:45.3195579Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:45.3195997Z env:
2025-03-04T20:05:45.3196244Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:45.3196684Z ##[endgroup]
2025-03-04T20:05:45.3475088Z Filesystem        Size  Used Avail Use% Mounted on
2025-03-04T20:05:45.3475670Z devtmpfs          4.2M     0  4.2M   0% /dev
2025-03-04T20:05:45.3476043Z tmpfs             8.2G     0  8.2G   0% /dev/shm
2025-03-04T20:05:45.3476400Z tmpfs             3.3G  488k  3.3G   1% /run
2025-03-04T20:05:45.3476929Z /dev/nvme0n1p1    161G   25G  137G  16% /
2025-03-04T20:05:45.3477298Z tmpfs             8.2G   13k  8.2G   1% /tmp
2025-03-04T20:05:45.3477676Z /dev/nvme0n1p128   11M  1.4M  9.2M  13% /boot/efi
2025-03-04T20:05:45.3591778Z Prepare all required actions
2025-03-04T20:05:45.3592207Z Getting action download info
2025-03-04T20:05:45.5032553Z ##[group]Run ./.github/actions/download-td-artifacts
2025-03-04T20:05:45.5032965Z with:
2025-03-04T20:05:45.5033196Z env:
2025-03-04T20:05:45.5033443Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:45.5033731Z ##[endgroup]
2025-03-04T20:05:45.5167060Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-04T20:05:45.5167465Z with:
2025-03-04T20:05:45.5167707Z   name: td_results
2025-03-04T20:05:45.5167981Z   s3-bucket: gha-artifacts
2025-03-04T20:05:45.5168280Z   region: us-east-1
2025-03-04T20:05:45.5168529Z env:
2025-03-04T20:05:45.5168767Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:45.5169056Z ##[endgroup]
2025-03-04T20:05:45.9689423Z (node:40830) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-04T20:05:45.9689973Z 
2025-03-04T20:05:45.9690271Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-04T20:05:45.9690848Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-04T20:05:45.9691445Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-04T20:05:46.0668878Z Found 1 objects with prefix pytorch/pytorch/13661694839/td_results/
2025-03-04T20:05:46.0669585Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json
2025-03-04T20:05:46.1216729Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json
2025-03-04T20:05:46.1221880Z Artifact download has finished successfully
2025-03-04T20:05:46.1514897Z ##[group]Run mkdir -p .additional_ci_files
2025-03-04T20:05:46.1515303Z mkdir -p .additional_ci_files
2025-03-04T20:05:46.1515776Z mv td_results.json .additional_ci_files/td_results.json || true
2025-03-04T20:05:46.1522191Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:46.1522611Z env:
2025-03-04T20:05:46.1522875Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:46.1523169Z ##[endgroup]
2025-03-04T20:05:46.1991665Z ##[group]Run .github/scripts/parse_ref.py
2025-03-04T20:05:46.1992089Z .github/scripts/parse_ref.py
2025-03-04T20:05:46.1997950Z shell: /usr/bin/bash -e {0}
2025-03-04T20:05:46.1998244Z env:
2025-03-04T20:05:46.1998490Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:46.1998782Z ##[endgroup]
2025-03-04T20:05:46.2404622Z Prepare all required actions
2025-03-04T20:05:46.2405513Z Getting action download info
2025-03-04T20:05:46.3855385Z ##[group]Run ./.github/actions/filter-test-configs
2025-03-04T20:05:46.3855776Z with:
2025-03-04T20:05:46.3856206Z   github-token: ***
2025-03-04T20:05:46.3859082Z   test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}
2025-03-04T20:05:46.3862200Z   job-name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:05:46.3862714Z env:
2025-03-04T20:05:46.3862955Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:46.3863243Z ##[endgroup]
2025-03-04T20:05:46.3950607Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-04T20:05:46.3950951Z with:
2025-03-04T20:05:46.3951191Z   shell: bash
2025-03-04T20:05:46.3951445Z   timeout_minutes: 10
2025-03-04T20:05:46.3951719Z   max_attempts: 5
2025-03-04T20:05:46.3951984Z   retry_wait_seconds: 30
2025-03-04T20:05:46.3952826Z   command: set -eux
# PyYAML 6.0 doesn't work with MacOS x86 anymore
# This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2
python3 -m pip install requests==2.27.1 pyyaml==6.0.1

2025-03-04T20:05:46.3953707Z   polling_interval_seconds: 1
2025-03-04T20:05:46.3954030Z   warning_on_retry: true
2025-03-04T20:05:46.3954319Z   continue_on_error: false
2025-03-04T20:05:46.3954617Z env:
2025-03-04T20:05:46.3954856Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:46.3955434Z   GITHUB_TOKEN: ***
2025-03-04T20:05:46.3955696Z ##[endgroup]
2025-03-04T20:05:46.5170908Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1
2025-03-04T20:05:46.7499416Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T20:05:46.9398228Z Collecting requests==2.27.1
2025-03-04T20:05:46.9620289Z   Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
2025-03-04T20:05:47.1849343Z Collecting pyyaml==6.0.1
2025-03-04T20:05:47.1887227Z   Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB)
2025-03-04T20:05:47.2621838Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10)
2025-03-04T20:05:47.5983390Z Collecting charset-normalizer~=2.0.0
2025-03-04T20:05:47.6022761Z   Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
2025-03-04T20:05:47.6308508Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10)
2025-03-04T20:05:47.6969196Z Collecting certifi>=2017.4.17
2025-03-04T20:05:47.7011738Z   Downloading certifi-2025.1.31-py3-none-any.whl (166 kB)
2025-03-04T20:05:47.8128147Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml
2025-03-04T20:05:48.1016324Z Successfully installed certifi-2025.1.31 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1
2025-03-04T20:05:48.4725939Z Command completed after 1 attempt(s).
2025-03-04T20:05:48.4782084Z ##[group]Run set -x
2025-03-04T20:05:48.4782386Z set -x
2025-03-04T20:05:48.4782644Z 
2025-03-04T20:05:48.4783068Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-04T20:05:48.4783597Z # in runner workspace
2025-03-04T20:05:48.4784200Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2025-03-04T20:05:48.4790117Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:48.4790534Z env:
2025-03-04T20:05:48.4790782Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:48.4791072Z ##[endgroup]
2025-03-04T20:05:48.4814967Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2025-03-04T20:05:48.5047607Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-04T20:05:48.5048076Z echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-04T20:05:48.5048457Z echo "Job name: ${JOB_NAME}"
2025-03-04T20:05:48.5048784Z 
2025-03-04T20:05:48.5049205Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-04T20:05:48.5049731Z # in runner workspace
2025-03-04T20:05:48.5050199Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2025-03-04T20:05:48.5050748Z   --workflow "${GITHUB_WORKFLOW}" \
2025-03-04T20:05:48.5051123Z   --job-name "${JOB_NAME}" \
2025-03-04T20:05:48.5054084Z   --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}" \
2025-03-04T20:05:48.5056949Z   --selected-test-configs "" \
2025-03-04T20:05:48.5057325Z   --pr-number "${PR_NUMBER}" \
2025-03-04T20:05:48.5057660Z   --tag "${TAG}" \
2025-03-04T20:05:48.5058093Z   --event-name "${EVENT_NAME}" \
2025-03-04T20:05:48.5058455Z   --schedule "${SCHEDULE}" \
2025-03-04T20:05:48.5058799Z   --branch "${HEAD_BRANCH}"
2025-03-04T20:05:48.5064725Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:48.5065121Z env:
2025-03-04T20:05:48.5065365Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:48.5065866Z   GITHUB_TOKEN: ***
2025-03-04T20:05:48.5066311Z   JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:05:48.5066829Z   PR_NUMBER: 148205
2025-03-04T20:05:48.5067156Z   TAG: 
2025-03-04T20:05:48.5067449Z   EVENT_NAME: pull_request
2025-03-04T20:05:48.5067736Z   SCHEDULE: 
2025-03-04T20:05:48.5067980Z   HEAD_BRANCH: 
2025-03-04T20:05:48.5068236Z ##[endgroup]
2025-03-04T20:05:48.5091108Z Workflow: pull
2025-03-04T20:05:48.5091597Z Job name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:05:48.7253533Z INFO:root:Found no test-config label on the PR, so all test configs are included
2025-03-04T20:05:48.8647672Z ##[group]Run echo "Filtered matrix:"
2025-03-04T20:05:48.8648082Z echo "Filtered matrix:"
2025-03-04T20:05:48.8651053Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}"
2025-03-04T20:05:48.8653859Z 
2025-03-04T20:05:48.8654099Z echo
2025-03-04T20:05:48.8654408Z echo "Is the current job unstable? False"
2025-03-04T20:05:48.8654768Z 
2025-03-04T20:05:48.8655010Z echo
2025-03-04T20:05:48.8655307Z echo "Is keep-going label set? False"
2025-03-04T20:05:48.8655660Z 
2025-03-04T20:05:48.8655895Z echo
2025-03-04T20:05:48.8656159Z echo "Renabled issues? "
2025-03-04T20:05:48.8661993Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:48.8662402Z env:
2025-03-04T20:05:48.8662692Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:48.8662982Z ##[endgroup]
2025-03-04T20:05:48.8685815Z Filtered matrix:
2025-03-04T20:05:48.8689643Z {include: [{config: default, shard: 1, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: lf.linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: lf.linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: lf.linux.2xlarge}]}
2025-03-04T20:05:48.8692475Z 
2025-03-04T20:05:48.8692616Z Is the current job unstable? False
2025-03-04T20:05:48.8692850Z 
2025-03-04T20:05:48.8692971Z Is keep-going label set? False
2025-03-04T20:05:48.8693185Z 
2025-03-04T20:05:48.8693289Z Renabled issues? 
2025-03-04T20:05:48.8738036Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-04T20:05:48.8738635Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-04T20:05:48.8744294Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T20:05:48.8744710Z env:
2025-03-04T20:05:48.8744956Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:48.8745250Z   JOB_TIMEOUT: 600
2025-03-04T20:05:48.8745500Z ##[endgroup]
2025-03-04T20:05:48.8815194Z ##[group]Run set -x
2025-03-04T20:05:48.8815572Z set -x
2025-03-04T20:05:48.8815825Z 
2025-03-04T20:05:48.8816095Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2025-03-04T20:05:48.8816527Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2025-03-04T20:05:48.8816957Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2025-03-04T20:05:48.8817354Z   TEST_COMMAND=.ci/onnx/test.sh
2025-03-04T20:05:48.8817687Z else
2025-03-04T20:05:48.8818076Z   TEST_COMMAND=.ci/pytorch/test.sh
2025-03-04T20:05:48.8818419Z fi
2025-03-04T20:05:48.8818665Z 
2025-03-04T20:05:48.8818963Z # Leaving 1GB for the runner and other things
2025-03-04T20:05:48.8819594Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo)
2025-03-04T20:05:48.8820538Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap
2025-03-04T20:05:48.8821295Z # comes from https://github.com/pytorch/test-infra/pull/6058
2025-03-04T20:05:48.8821868Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3))
2025-03-04T20:05:48.8822326Z 
2025-03-04T20:05:48.8822621Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-04T20:05:48.8823001Z   SHM_OPTS=
2025-03-04T20:05:48.8823297Z   JENKINS_USER=
2025-03-04T20:05:48.8823681Z   # ensure that docker container cleanly exits in 12 hours
2025-03-04T20:05:48.8824189Z   # if for some reason cleanup action doesn't stop container
2025-03-04T20:05:48.8824631Z   # when job is cancelled
2025-03-04T20:05:48.8824977Z   DOCKER_SHELL_CMD="sleep 12h"
2025-03-04T20:05:48.8825311Z 
2025-03-04T20:05:48.8825718Z   # since some steps are skipped on s390x, if they are necessary, run them here
2025-03-04T20:05:48.8826304Z   env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T20:05:48.8826786Z   env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T20:05:48.8827171Z else
2025-03-04T20:05:48.8827453Z   SHM_OPTS="--shm-size=${SHM_SIZE}"
2025-03-04T20:05:48.8827824Z   JENKINS_USER="--user jenkins"
2025-03-04T20:05:48.8828169Z   DOCKER_SHELL_CMD=
2025-03-04T20:05:48.8828459Z fi
2025-03-04T20:05:48.8828690Z 
2025-03-04T20:05:48.8829062Z # detached container should get cleaned up by teardown_ec2_linux
2025-03-04T20:05:48.8829634Z # TODO: Stop building test binaries as part of the build phase
2025-03-04T20:05:48.8830441Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice
2025-03-04T20:05:48.8831018Z # shellcheck disable=SC2086,SC2090
2025-03-04T20:05:48.8831391Z container_name=$(docker run \
2025-03-04T20:05:48.8831738Z   ${GPU_FLAG:-} \
2025-03-04T20:05:48.8832079Z   ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
2025-03-04T20:05:48.8832459Z   -e BUILD_ENVIRONMENT \
2025-03-04T20:05:48.8832785Z   -e PR_NUMBER \
2025-03-04T20:05:48.8833086Z   -e GITHUB_ACTIONS \
2025-03-04T20:05:48.8833394Z   -e GITHUB_REPOSITORY \
2025-03-04T20:05:48.8833732Z   -e GITHUB_WORKFLOW \
2025-03-04T20:05:48.8834050Z   -e GITHUB_JOB \
2025-03-04T20:05:48.8834349Z   -e GITHUB_RUN_ID \
2025-03-04T20:05:48.8834658Z   -e GITHUB_RUN_NUMBER \
2025-03-04T20:05:48.8834987Z   -e GITHUB_RUN_ATTEMPT \
2025-03-04T20:05:48.8835312Z   -e JOB_ID \
2025-03-04T20:05:48.8835593Z   -e JOB_NAME \
2025-03-04T20:05:48.8835888Z   -e BASE_SHA \
2025-03-04T20:05:48.8836175Z   -e BRANCH \
2025-03-04T20:05:48.8836460Z   -e SHA1 \
2025-03-04T20:05:48.8836743Z   -e AWS_DEFAULT_REGION \
2025-03-04T20:05:48.8837166Z   -e IN_WHEEL_TEST \
2025-03-04T20:05:48.8837477Z   -e SHARD_NUMBER \
2025-03-04T20:05:48.8837781Z   -e TEST_CONFIG \
2025-03-04T20:05:48.8838089Z   -e NUM_TEST_SHARDS \
2025-03-04T20:05:48.8838412Z   -e REENABLED_ISSUES \
2025-03-04T20:05:48.8838752Z   -e CONTINUE_THROUGH_ERROR \
2025-03-04T20:05:48.8839091Z   -e VERBOSE_TEST_LOGS \
2025-03-04T20:05:48.8839421Z   -e TEST_SHOWLOCALS \
2025-03-04T20:05:48.8839743Z   -e NO_TEST_TIMEOUT \
2025-03-04T20:05:48.8840051Z   -e NO_TD \
2025-03-04T20:05:48.8840337Z   -e TD_DISTRIBUTED \
2025-03-04T20:05:48.8840658Z   -e PR_LABELS \
2025-03-04T20:05:48.8840985Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2025-03-04T20:05:48.8841354Z   -e SCCACHE_BUCKET \
2025-03-04T20:05:48.8841672Z   -e SCCACHE_REGION \
2025-03-04T20:05:48.8841982Z   -e XLA_CUDA \
2025-03-04T20:05:48.8842307Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2025-03-04T20:05:48.8842714Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2025-03-04T20:05:48.8843126Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2025-03-04T20:05:48.8843537Z   -e SKIP_SCCACHE_INITIALIZATION=1 \
2025-03-04T20:05:48.8843920Z   -e HUGGING_FACE_HUB_TOKEN \
2025-03-04T20:05:48.8844292Z   -e SCRIBE_GRAPHQL_ACCESS_TOKEN \
2025-03-04T20:05:48.8844654Z   -e DASHBOARD_TAG \
2025-03-04T20:05:48.8844972Z   -e IS_A100_RUNNER \
2025-03-04T20:05:48.8845299Z   -e ARTIFACTS_FILE_SUFFIX \
2025-03-04T20:05:48.8845702Z   --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \
2025-03-04T20:05:48.8846156Z   --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \
2025-03-04T20:05:48.8846653Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2025-03-04T20:05:48.8847084Z   --security-opt seccomp=unconfined \
2025-03-04T20:05:48.8847459Z   --cap-add=SYS_PTRACE \
2025-03-04T20:05:48.8847784Z   --ipc=host \
2025-03-04T20:05:48.8848067Z   ${SHM_OPTS} \
2025-03-04T20:05:48.8848350Z   --tty \
2025-03-04T20:05:48.8848617Z   --detach \
2025-03-04T20:05:48.8848910Z   --name="${container_name}" \
2025-03-04T20:05:48.8849255Z   ${JENKINS_USER} \
2025-03-04T20:05:48.8849635Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2025-03-04T20:05:48.8850069Z   -w /var/lib/jenkins/workspace \
2025-03-04T20:05:48.8850424Z   "${DOCKER_IMAGE}" \
2025-03-04T20:05:48.8850736Z   ${DOCKER_SHELL_CMD}
2025-03-04T20:05:48.8851545Z )
2025-03-04T20:05:48.8851870Z # Propagate download.pytorch.org IP to container
2025-03-04T20:05:48.8852594Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
2025-03-04T20:05:48.8853358Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2025-03-04T20:05:48.8868906Z 
2025-03-04T20:05:48.8869290Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-04T20:05:48.8869938Z   docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt"
2025-03-04T20:05:48.8870506Z fi
2025-03-04T20:05:48.8870751Z 
2025-03-04T20:05:48.8871288Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2025-03-04T20:05:48.8877403Z shell: /usr/bin/bash -e {0}
2025-03-04T20:05:48.8877711Z env:
2025-03-04T20:05:48.8877959Z   GIT_DEFAULT_BRANCH: main
2025-03-04T20:05:48.8878316Z   BUILD_ENVIRONMENT: linux-focal-py3.13-clang10
2025-03-04T20:05:48.8878696Z   PR_NUMBER: 148205
2025-03-04T20:05:48.8878986Z   GITHUB_REPOSITORY: pytorch/pytorch
2025-03-04T20:05:48.8879324Z   GITHUB_WORKFLOW: pull
2025-03-04T20:05:48.8879598Z   GITHUB_JOB: test
2025-03-04T20:05:48.8879869Z   GITHUB_RUN_ID: 13661694839
2025-03-04T20:05:48.8880306Z   GITHUB_RUN_NUMBER: 295855
2025-03-04T20:05:48.8880612Z   GITHUB_RUN_ATTEMPT: 1
2025-03-04T20:05:48.8880888Z   JOB_ID: 38194769830
2025-03-04T20:05:48.8881341Z   JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:05:48.8881846Z   BRANCH: pull/148205
2025-03-04T20:05:48.8882156Z   SHA1: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:05:48.8882561Z   BASE_SHA: 6130f46efa5539abfeee284d298e5696e18b0475
2025-03-04T20:05:48.8882941Z   TEST_CONFIG: dynamo_wrapped
2025-03-04T20:05:48.8883239Z   SHARD_NUMBER: 1
2025-03-04T20:05:48.8883494Z   NUM_TEST_SHARDS: 3
2025-03-04T20:05:48.8883758Z   REENABLED_ISSUES: 
2025-03-04T20:05:48.8884043Z   CONTINUE_THROUGH_ERROR: False
2025-03-04T20:05:48.8884356Z   VERBOSE_TEST_LOGS: False
2025-03-04T20:05:48.8884652Z   TEST_SHOWLOCALS: False
2025-03-04T20:05:48.8884938Z   NO_TEST_TIMEOUT: False
2025-03-04T20:05:48.8885215Z   NO_TD: False
2025-03-04T20:05:48.8885473Z   TD_DISTRIBUTED: False
2025-03-04T20:05:48.8885818Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2025-03-04T20:05:48.8886214Z   SCCACHE_REGION: us-east-1
2025-03-04T20:05:48.8886502Z   SHM_SIZE: 1g
2025-03-04T20:05:48.8887269Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:05:48.8888034Z   XLA_CUDA: 
2025-03-04T20:05:48.8888431Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2025-03-04T20:05:48.8888927Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2025-03-04T20:05:48.8889269Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2025-03-04T20:05:48.8889602Z   DASHBOARD_TAG: 
2025-03-04T20:05:48.8890073Z   HUGGING_FACE_HUB_TOKEN: ***
2025-03-04T20:05:48.8890537Z   SCRIBE_GRAPHQL_ACCESS_TOKEN: ***
2025-03-04T20:05:48.8890846Z   IS_A100_RUNNER: 0
2025-03-04T20:05:48.8891265Z   ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T20:05:48.8891754Z ##[endgroup]
2025-03-04T20:05:48.8914543Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]]
2025-03-04T20:05:48.8915195Z + [[ linux-focal-py3.13-clang10 == *onnx* ]]
2025-03-04T20:05:48.8915570Z + TEST_COMMAND=.ci/pytorch/test.sh
2025-03-04T20:05:48.8917985Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo
2025-03-04T20:05:48.8935942Z + TOTAL_AVAILABLE_MEMORY_IN_GB='14.244 '
2025-03-04T20:05:48.8936560Z + TOTAL_MEMORY_WITH_SWAP=17
2025-03-04T20:05:48.8937187Z + [[ linux-focal-py3.13-clang10 == *\s\3\9\0\x* ]]
2025-03-04T20:05:48.8937638Z + SHM_OPTS=--shm-size=1g
2025-03-04T20:05:48.8938079Z + JENKINS_USER='--user jenkins'
2025-03-04T20:05:48.8938387Z + DOCKER_SHELL_CMD=
2025-03-04T20:05:48.8945391Z +++ nproc --ignore=2
2025-03-04T20:05:48.8969293Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=14g --memory-swap=17g --env-file=/tmp/github_env_13661694839 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T20:05:54.4386199Z + container_name=4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T20:05:54.4388629Z + grep download.pytorch.org /etc/hosts
2025-03-04T20:05:54.4389932Z + docker exec -i 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57 sudo bash -c '/bin/cat >> /etc/hosts'
2025-03-04T20:05:54.6297619Z + echo DOCKER_CONTAINER_ID=4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T20:05:54.6298340Z + [[ linux-focal-py3.13-clang10 == *\s\3\9\0\x* ]]
2025-03-04T20:05:54.6300004Z ++ echo dist/torch-2.7.0a0+git1b74980-cp313-cp313-linux_x86_64.whl
2025-03-04T20:05:54.6302199Z + docker exec -t 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57 sh -c 'python3 -m pip install dist/torch-2.7.0a0+git1b74980-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh'
2025-03-04T20:05:55.1889090Z Processing ./dist/torch-2.7.0a0+git1b74980-cp313-cp313-linux_x86_64.whl (from torch==2.7.0a0+git1b74980)
2025-03-04T20:05:55.6171927Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.16.1)
2025-03-04T20:05:55.6175163Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (4.12.2)
2025-03-04T20:05:55.6186480Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (75.8.0)
2025-03-04T20:05:55.6190312Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.13.3)
2025-03-04T20:05:55.6193516Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2.8.8)
2025-03-04T20:05:55.6196463Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.1.5)
2025-03-04T20:05:55.6199507Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2024.10.0)
2025-03-04T20:05:55.6215219Z 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==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.3.0)
2025-03-04T20:05:55.6231361Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.3.0)
2025-03-04T20:05:55.6245167Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2.1.2)
2025-03-04T20:05:55.6349232Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.0.2)
2025-03-04T20:05:55.8332341Z Installing collected packages: torch
2025-03-04T20:06:06.1773939Z Successfully installed torch-2.7.0a0+git1b74980
2025-03-04T20:06:06.2844290Z + export TERM=vt100
2025-03-04T20:06:06.2844771Z + TERM=vt100
2025-03-04T20:06:06.2846006Z ++ dirname .ci/pytorch/test.sh
2025-03-04T20:06:06.2864374Z + source .ci/pytorch/common.sh
2025-03-04T20:06:06.2871061Z +++ dirname .ci/pytorch/common.sh
2025-03-04T20:06:06.2877366Z ++ source .ci/pytorch/common_utils.sh
2025-03-04T20:06:06.2883757Z +++ declare -f -t trap_add
2025-03-04T20:06:06.2890359Z ++ set -ex -o pipefail
2025-03-04T20:06:06.2890997Z ++ [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-04T20:06:06.2891519Z ++ BUILD_TEST_LIBTORCH=0
2025-03-04T20:06:06.2891941Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-04T20:06:06.2892331Z + [[ linux-focal-py3.13-clang10 != *s390x* ]]
2025-03-04T20:06:06.2892710Z + [[ -d /var/lib/jenkins/workspace ]]
2025-03-04T20:06:06.2894910Z ++ stat -c %u /var/lib/jenkins/workspace
2025-03-04T20:06:06.2935743Z + WORKSPACE_ORIGINAL_OWNER_ID=1000
2025-03-04T20:06:06.2936138Z + trap_add cleanup_workspace EXIT
2025-03-04T20:06:06.2936485Z + trap_add_cmd=cleanup_workspace
2025-03-04T20:06:06.2936798Z + shift
2025-03-04T20:06:06.2937054Z + for trap_add_name in "$@"
2025-03-04T20:06:06.2942079Z +++ trap -p EXIT
2025-03-04T20:06:06.2944807Z ++ eval 'extract_trap_cmd '
2025-03-04T20:06:06.2945204Z +++ extract_trap_cmd
2025-03-04T20:06:06.2945533Z +++ printf '%s\n' ''
2025-03-04T20:06:06.2945833Z ++ printf '%s\n' cleanup_workspace
2025-03-04T20:06:06.2947586Z + trap -- '
2025-03-04T20:06:06.2947884Z cleanup_workspace' EXIT
2025-03-04T20:06:06.2948250Z + sudo chown -R jenkins /var/lib/jenkins/workspace
2025-03-04T20:06:06.7961775Z + git config --global --add safe.directory /var/lib/jenkins/workspace
2025-03-04T20:06:06.8143882Z + echo 'Environment variables:'
2025-03-04T20:06:06.8144439Z Environment variables:
2025-03-04T20:06:06.8144725Z + env
2025-03-04T20:06:06.8162960Z INSTALLED_DB=yes
2025-03-04T20:06:06.8163882Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T20:06:06.8164559Z CONTINUE_THROUGH_ERROR=False
2025-03-04T20:06:06.8165099Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10
2025-03-04T20:06:06.8165521Z HOSTNAME=4b477976dd7b
2025-03-04T20:06:06.8166195Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8166945Z GITHUB_ACTION=__self
2025-03-04T20:06:06.8167301Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-04T20:06:06.8167633Z GITHUB_RUN_NUMBER=295855
2025-03-04T20:06:06.8167976Z TEST_CONFIG=dynamo_wrapped
2025-03-04T20:06:06.8168312Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-04T20:06:06.8168723Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-04T20:06:06.8169189Z IS_A100_RUNNER=0
2025-03-04T20:06:06.8169763Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-04T20:06:06.8170119Z GITHUB_TRIGGERING_ACTOR=williamwen42
2025-03-04T20:06:06.8170505Z GITHUB_REF_TYPE=branch
2025-03-04T20:06:06.8170809Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-04T20:06:06.8171225Z BASE_SHA=6130f46efa5539abfeee284d298e5696e18b0475
2025-03-04T20:06:06.8171590Z XLA_CUDA=
2025-03-04T20:06:06.8172031Z HUGGING_FACE_HUB_TOKEN=***
2025-03-04T20:06:06.8172571Z ***
2025-03-04T20:06:06.8172819Z GITHUB_REPOSITORY_ID=65600975
2025-03-04T20:06:06.8173135Z GITHUB_ACTIONS=true
2025-03-04T20:06:06.8173489Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:06:06.8174136Z GITHUB_SHA=d0654836237b89031de4353648b2c86ba3fc52f9
2025-03-04T20:06:06.8174772Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/148205/merge
2025-03-04T20:06:06.8175582Z UCC_HOME=/usr
2025-03-04T20:06:06.8175838Z VERBOSE_TEST_LOGS=False
2025-03-04T20:06:06.8176189Z GITHUB_REF=refs/pull/148205/merge
2025-03-04T20:06:06.8176507Z SHARD_NUMBER=1
2025-03-04T20:06:06.8176773Z GITHUB_REF_PROTECTED=false
2025-03-04T20:06:06.8177126Z HOME=/var/lib/jenkins
2025-03-04T20:06:06.8177435Z GITHUB_API_URL=https://api.github.com
2025-03-04T20:06:06.8177935Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-04T20:06:06.8178245Z UCX_COMMIT=
2025-03-04T20:06:06.8178540Z NUM_TEST_SHARDS=3
2025-03-04T20:06:06.8178796Z UCX_HOME=/usr
2025-03-04T20:06:06.8179466Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8180391Z JOB_NAME=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:06:06.8181294Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8182216Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-04T20:06:06.8182770Z GITHUB_EVENT_NAME=pull_request
2025-03-04T20:06:06.8183131Z DASHBOARD_TAG=
2025-03-04T20:06:06.8183390Z GITHUB_RUN_ID=13661694839
2025-03-04T20:06:06.8184068Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8184935Z GITHUB_ACTOR=williamwen42
2025-03-04T20:06:06.8185284Z PR_NUMBER=148205
2025-03-04T20:06:06.8185541Z DESIRED_CUDA=
2025-03-04T20:06:06.8185793Z GITHUB_RUN_ATTEMPT=1
2025-03-04T20:06:06.8186121Z ANACONDA_PYTHON_VERSION=3.13
2025-03-04T20:06:06.8186495Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-04T20:06:06.8186878Z TERM=vt100
2025-03-04T20:06:06.8187111Z INSTALLED_VISION=yes
2025-03-04T20:06:06.8187386Z BRANCH=pull/148205
2025-03-04T20:06:06.8187661Z SCCACHE_REGION=us-east-1
2025-03-04T20:06:06.8187962Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-04T20:06:06.8188280Z CUDA_PATH=/usr/local/cuda
2025-03-04T20:06:06.8188846Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-04T20:06:06.8189532Z GITHUB_SERVER_URL=https://github.com
2025-03-04T20:06:06.8189864Z UCC_COMMIT=
2025-03-04T20:06:06.8190104Z REENABLED_ISSUES=
2025-03-04T20:06:06.8190357Z DOCS=
2025-03-04T20:06:06.8190581Z SHLVL=1
2025-03-04T20:06:06.8190809Z MAX_JOBS=6
2025-03-04T20:06:06.8191060Z GITHUB_ACTOR_ID=20119138
2025-03-04T20:06:06.8191444Z GITHUB_WORKFLOW_SHA=d0654836237b89031de4353648b2c86ba3fc52f9
2025-03-04T20:06:06.8191862Z GITHUB_REF_NAME=148205/merge
2025-03-04T20:06:06.8192305Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-04T20:06:06.8192800Z GITHUB_JOB=test
2025-03-04T20:06:06.8193062Z NO_TEST_TIMEOUT=False
2025-03-04T20:06:06.8193340Z TD_DISTRIBUTED=False
2025-03-04T20:06:06.8193640Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-04T20:06:06.8193980Z GITHUB_RETENTION_DAYS=90
2025-03-04T20:06:06.8194283Z OPENSSL_DIR=/opt/openssl
2025-03-04T20:06:06.8194593Z GITHUB_ACTION_REPOSITORY=
2025-03-04T20:06:06.8195421Z 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
2025-03-04T20:06:06.8196375Z GITHUB_BASE_REF=gh/williamwen42/215/base
2025-03-04T20:06:06.8196726Z INSTALLED_ACL=
2025-03-04T20:06:06.8197148Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T20:06:06.8197615Z CI=true
2025-03-04T20:06:06.8197868Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-04T20:06:06.8198192Z JOB_ID=38194769830
2025-03-04T20:06:06.8198463Z INSTALLED_PROTOBUF=yes
2025-03-04T20:06:06.8198775Z GITHUB_HEAD_REF=gh/williamwen42/215/head
2025-03-04T20:06:06.8199122Z GITHUB_ACTION_REF=
2025-03-04T20:06:06.8199443Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-04T20:06:06.8199827Z TEST_SHOWLOCALS=False
2025-03-04T20:06:06.8200105Z GITHUB_WORKFLOW=pull
2025-03-04T20:06:06.8200393Z DEBIAN_FRONTEND=noninteractive
2025-03-04T20:06:06.8201162Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8201846Z NO_TD=False
2025-03-04T20:06:06.8202107Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-04T20:06:06.8202411Z _=/usr/bin/env
2025-03-04T20:06:06.8202757Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2025-03-04T20:06:06.8314432Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch
2025-03-04T20:06:06.8315166Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin
2025-03-04T20:06:06.8316015Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib
2025-03-04T20:06:06.8316928Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test
2025-03-04T20:06:06.8317527Z + BUILD_DIR=build
2025-03-04T20:06:06.8317795Z + BUILD_RENAMED_DIR=build_renamed
2025-03-04T20:06:06.8318126Z + BUILD_BIN_DIR=build/bin
2025-03-04T20:06:06.8318414Z + SHARD_NUMBER=1
2025-03-04T20:06:06.8318681Z + NUM_TEST_SHARDS=3
2025-03-04T20:06:06.8318967Z + export TORCH_SERIALIZATION_DEBUG=1
2025-03-04T20:06:06.8319314Z + TORCH_SERIALIZATION_DEBUG=1
2025-03-04T20:06:06.8319624Z + export VALGRIND=ON
2025-03-04T20:06:06.8319892Z + VALGRIND=ON
2025-03-04T20:06:06.8320186Z + [[ linux-focal-py3.13-clang10 == *clang9* ]]
2025-03-04T20:06:06.8320936Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-04T20:06:06.8321434Z + [[ linux-focal-py3.13-clang10 == *s390x* ]]
2025-03-04T20:06:06.8321786Z + [[ 0 == \1 ]]
2025-03-04T20:06:06.8322041Z + [[ False == \1 ]]
2025-03-04T20:06:06.8322394Z + [[ linux-focal-py3.13-clang10 != *bazel* ]]
2025-03-04T20:06:06.8322776Z ++ realpath build/custom_test_artifacts
2025-03-04T20:06:06.8344086Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2025-03-04T20:06:06.8344634Z + [[ -n '' ]]
2025-03-04T20:06:06.8344906Z + echo 'Environment variables'
2025-03-04T20:06:06.8345211Z Environment variables
2025-03-04T20:06:06.8345479Z + env
2025-03-04T20:06:06.8351188Z INSTALLED_DB=yes
2025-03-04T20:06:06.8351863Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T20:06:06.8352587Z CONTINUE_THROUGH_ERROR=False
2025-03-04T20:06:06.8353087Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10
2025-03-04T20:06:06.8353503Z HOSTNAME=4b477976dd7b
2025-03-04T20:06:06.8354399Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8355085Z GITHUB_ACTION=__self
2025-03-04T20:06:06.8355386Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-04T20:06:06.8355725Z GITHUB_RUN_NUMBER=295855
2025-03-04T20:06:06.8356020Z TEST_CONFIG=dynamo_wrapped
2025-03-04T20:06:06.8356332Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-04T20:06:06.8356690Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-04T20:06:06.8357025Z IS_A100_RUNNER=0
2025-03-04T20:06:06.8357529Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-04T20:06:06.8357862Z GITHUB_TRIGGERING_ACTOR=williamwen42
2025-03-04T20:06:06.8358208Z GITHUB_REF_TYPE=branch
2025-03-04T20:06:06.8358500Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-04T20:06:06.8358848Z BASE_SHA=6130f46efa5539abfeee284d298e5696e18b0475
2025-03-04T20:06:06.8359212Z XLA_CUDA=
2025-03-04T20:06:06.8359569Z HUGGING_FACE_HUB_TOKEN=***
2025-03-04T20:06:06.8360087Z ***
2025-03-04T20:06:06.8360338Z GITHUB_REPOSITORY_ID=65600975
2025-03-04T20:06:06.8360646Z GITHUB_ACTIONS=true
2025-03-04T20:06:06.8360954Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T20:06:06.8361358Z GITHUB_SHA=d0654836237b89031de4353648b2c86ba3fc52f9
2025-03-04T20:06:06.8361928Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/148205/merge
2025-03-04T20:06:06.8362455Z UCC_HOME=/usr
2025-03-04T20:06:06.8362717Z TORCH_SERIALIZATION_DEBUG=1
2025-03-04T20:06:06.8363029Z VERBOSE_TEST_LOGS=False
2025-03-04T20:06:06.8363324Z GITHUB_REF=refs/pull/148205/merge
2025-03-04T20:06:06.8363636Z SHARD_NUMBER=1
2025-03-04T20:06:06.8363901Z GITHUB_REF_PROTECTED=false
2025-03-04T20:06:06.8364386Z HOME=/var/lib/jenkins
2025-03-04T20:06:06.8364699Z GITHUB_API_URL=https://api.github.com
2025-03-04T20:06:06.8365065Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-04T20:06:06.8365391Z UCX_COMMIT=
2025-03-04T20:06:06.8365637Z NUM_TEST_SHARDS=3
2025-03-04T20:06:06.8365882Z UCX_HOME=/usr
2025-03-04T20:06:06.8366545Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8367414Z JOB_NAME=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T20:06:06.8368264Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8369128Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-04T20:06:06.8369685Z GITHUB_EVENT_NAME=pull_request
2025-03-04T20:06:06.8369999Z DASHBOARD_TAG=
2025-03-04T20:06:06.8370264Z GITHUB_RUN_ID=13661694839
2025-03-04T20:06:06.8370952Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8371687Z GITHUB_ACTOR=williamwen42
2025-03-04T20:06:06.8371978Z PR_NUMBER=148205
2025-03-04T20:06:06.8372236Z DESIRED_CUDA=
2025-03-04T20:06:06.8372494Z GITHUB_RUN_ATTEMPT=1
2025-03-04T20:06:06.8372764Z VALGRIND=ON
2025-03-04T20:06:06.8373125Z ANACONDA_PYTHON_VERSION=3.13
2025-03-04T20:06:06.8373506Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-04T20:06:06.8374160Z TERM=vt100
2025-03-04T20:06:06.8374407Z INSTALLED_VISION=yes
2025-03-04T20:06:06.8374688Z BRANCH=pull/148205
2025-03-04T20:06:06.8374964Z SCCACHE_REGION=us-east-1
2025-03-04T20:06:06.8375267Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-04T20:06:06.8375572Z CUDA_PATH=/usr/local/cuda
2025-03-04T20:06:06.8376137Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-04T20:06:06.8376763Z GITHUB_SERVER_URL=https://github.com
2025-03-04T20:06:06.8377098Z UCC_COMMIT=
2025-03-04T20:06:06.8377355Z REENABLED_ISSUES=
2025-03-04T20:06:06.8377610Z DOCS=
2025-03-04T20:06:06.8377912Z SHLVL=1
2025-03-04T20:06:06.8378140Z MAX_JOBS=6
2025-03-04T20:06:06.8378388Z GITHUB_ACTOR_ID=20119138
2025-03-04T20:06:06.8378762Z GITHUB_WORKFLOW_SHA=d0654836237b89031de4353648b2c86ba3fc52f9
2025-03-04T20:06:06.8379177Z GITHUB_REF_NAME=148205/merge
2025-03-04T20:06:06.8379627Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-04T20:06:06.8380097Z GITHUB_JOB=test
2025-03-04T20:06:06.8380357Z NO_TEST_TIMEOUT=False
2025-03-04T20:06:06.8380636Z TD_DISTRIBUTED=False
2025-03-04T20:06:06.8380932Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-04T20:06:06.8381254Z GITHUB_RETENTION_DAYS=90
2025-03-04T20:06:06.8381552Z OPENSSL_DIR=/opt/openssl
2025-03-04T20:06:06.8381855Z GITHUB_ACTION_REPOSITORY=
2025-03-04T20:06:06.8382691Z 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
2025-03-04T20:06:06.8383585Z GITHUB_BASE_REF=gh/williamwen42/215/base
2025-03-04T20:06:06.8383929Z INSTALLED_ACL=
2025-03-04T20:06:06.8384341Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T20:06:06.8384817Z CI=true
2025-03-04T20:06:06.8385068Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-04T20:06:06.8385378Z JOB_ID=38194769830
2025-03-04T20:06:06.8385647Z INSTALLED_PROTOBUF=yes
2025-03-04T20:06:06.8385950Z GITHUB_HEAD_REF=gh/williamwen42/215/head
2025-03-04T20:06:06.8386294Z GITHUB_ACTION_REF=
2025-03-04T20:06:06.8386621Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-04T20:06:06.8387008Z TEST_SHOWLOCALS=False
2025-03-04T20:06:06.8387287Z GITHUB_WORKFLOW=pull
2025-03-04T20:06:06.8387565Z DEBIAN_FRONTEND=noninteractive
2025-03-04T20:06:06.8388237Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_b52ec390-d7db-4e1d-b598-875486f2b406
2025-03-04T20:06:06.8389016Z NO_TD=False
2025-03-04T20:06:06.8389281Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-04T20:06:06.8389741Z _=/usr/bin/env
2025-03-04T20:06:06.8390003Z + echo 'Testing pytorch'
2025-03-04T20:06:06.8390296Z Testing pytorch
2025-03-04T20:06:06.8390584Z + export LANG=C.UTF-8
2025-03-04T20:06:06.8390853Z + LANG=C.UTF-8
2025-03-04T20:06:06.8420254Z + PR_NUMBER=148205
2025-03-04T20:06:06.8420693Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]]
2025-03-04T20:06:06.8421278Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]]
2025-03-04T20:06:06.8421804Z + [[ dynamo_wrapped == \s\l\o\w ]]
2025-03-04T20:06:06.8422397Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]]
2025-03-04T20:06:06.8423059Z + [[ linux-focal-py3.13-clang10 == *cuda* ]]
2025-03-04T20:06:06.8423477Z + [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-04T20:06:06.8423913Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-04T20:06:06.8424278Z + [[ dynamo_wrapped == *crossref* ]]
2025-03-04T20:06:06.8424642Z + [[ linux-focal-py3.13-clang10 == *rocm* ]]
2025-03-04T20:06:06.8425031Z + [[ linux-focal-py3.13-clang10 == *xpu* ]]
2025-03-04T20:06:06.8425424Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]]
2025-03-04T20:06:06.8425806Z + pip_install --user ninja==1.10.2
2025-03-04T20:06:06.8426324Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T20:06:06.8426852Z + python3 -m pip install --progress-bar off --user ninja==1.10.2
2025-03-04T20:06:07.3947167Z Collecting ninja==1.10.2
2025-03-04T20:06:07.4361144Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2025-03-04T20:06:07.4465260Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2025-03-04T20:06:07.6198289Z Installing collected packages: ninja
2025-03-04T20:06:07.6273733Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2025-03-04T20:06:07.6274795Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2025-03-04T20:06:07.6339447Z Successfully installed ninja-1.10.2
2025-03-04T20:06:07.7257666Z + 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
2025-03-04T20:06:07.7259453Z + 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
2025-03-04T20:06:07.7260481Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]]
2025-03-04T20:06:07.7260852Z + install_tlparse
2025-03-04T20:06:07.7261149Z + pip_install --user tlparse==0.3.30
2025-03-04T20:06:07.7261595Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T20:06:07.7262137Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30
2025-03-04T20:06:08.1665209Z Collecting tlparse==0.3.30
2025-03-04T20:06:08.2008546Z   Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)
2025-03-04T20:06:08.2100331Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
2025-03-04T20:06:08.4077595Z Installing collected packages: tlparse
2025-03-04T20:06:08.4430834Z Successfully installed tlparse-0.3.30
2025-03-04T20:06:08.5394106Z ++ python -m site --user-base
2025-03-04T20:06:08.5540763Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-04T20:06:08.5542208Z + [[ linux-focal-py3.13-clang10 == *asan* ]]
2025-03-04T20:06:08.5542607Z + [[ linux-focal-py3.13-clang10 == *-debug* ]]
2025-03-04T20:06:08.5543003Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]]
2025-03-04T20:06:08.5543564Z + echo 'We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass'
2025-03-04T20:06:08.5544423Z We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass
2025-03-04T20:06:08.5545374Z + cd test
2025-03-04T20:06:08.5545759Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2025-03-04T20:06:10.0089332Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2025-03-04T20:06:10.0089809Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2025-03-04T20:06:10.0095009Z + DYNAMO_BENCHMARK_FLAGS=()
2025-03-04T20:06:10.0096299Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]]
2025-03-04T20:06:10.0097006Z + [[ dynamo_wrapped == *dynamo_eager* ]]
2025-03-04T20:06:10.0097438Z + [[ dynamo_wrapped == *aot_eager* ]]
2025-03-04T20:06:10.0097869Z + [[ dynamo_wrapped == *aot_inductor* ]]
2025-03-04T20:06:10.0098225Z + [[ dynamo_wrapped == *inductor* ]]
2025-03-04T20:06:10.0098568Z + [[ dynamo_wrapped == *dynamic* ]]
2025-03-04T20:06:10.0098906Z + [[ dynamo_wrapped == *cpu* ]]
2025-03-04T20:06:10.0099259Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
2025-03-04T20:06:10.0129837Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]]
2025-03-04T20:06:10.0130553Z + [[ linux-focal-py3.13-clang10 == *-bazel-* ]]
2025-03-04T20:06:10.0132925Z + cd test
2025-03-04T20:06:10.0133749Z + python -c 'import torch; print(torch.__config__.show())'
2025-03-04T20:06:11.1703462Z PyTorch built with:
2025-03-04T20:06:11.1703901Z   - GCC 4.2
2025-03-04T20:06:11.1704145Z   - C++ Version: 201703
2025-03-04T20:06:11.1704428Z   - clang 10.0.0
2025-03-04T20:06:11.1705363Z   - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-04T20:06:11.1706170Z   - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
2025-03-04T20:06:11.1706656Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-04T20:06:11.1707032Z   - LAPACK is enabled (usually provided by MKL)
2025-03-04T20:06:11.1707405Z   - NNPACK is enabled
2025-03-04T20:06:11.1707704Z   - CPU capability usage: AVX512
2025-03-04T20:06:11.1713204Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=1b7498080987913ecb3aff6253c5e88f3540d911, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -fcolor-diagnostics -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.7.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 
2025-03-04T20:06:11.1718775Z 
2025-03-04T20:06:11.4272432Z + cd test
2025-03-04T20:06:11.4272879Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2025-03-04T20:06:12.5724303Z ATen/Parallel:
2025-03-04T20:06:12.5724711Z 	at::get_num_threads() : 4
2025-03-04T20:06:12.5725050Z 	at::get_num_interop_threads() : 4
2025-03-04T20:06:12.5725399Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-04T20:06:12.5725714Z 	omp_get_max_threads() : 4
2025-03-04T20:06:12.5726323Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-04T20:06:12.5726964Z 	mkl_get_max_threads() : 4
2025-03-04T20:06:12.5727385Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
2025-03-04T20:06:12.5727884Z std::thread::hardware_concurrency() : 8
2025-03-04T20:06:12.5728233Z Environment variables:
2025-03-04T20:06:12.5728842Z 	OMP_NUM_THREADS : [not set]
2025-03-04T20:06:12.5729156Z 	MKL_NUM_THREADS : [not set]
2025-03-04T20:06:12.5729479Z ATen parallel backend: OpenMP
2025-03-04T20:06:12.5729699Z 
2025-03-04T20:06:12.8298736Z + [[ dynamo_wrapped == *numpy_2* ]]
2025-03-04T20:06:12.8299310Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]]
2025-03-04T20:06:12.8299833Z + [[ dynamo_wrapped == *backward* ]]
2025-03-04T20:06:12.8300177Z + [[ dynamo_wrapped == *xla* ]]
2025-03-04T20:06:12.8300531Z + [[ dynamo_wrapped == *executorch* ]]
2025-03-04T20:06:12.8300895Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]]
2025-03-04T20:06:12.8301287Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]]
2025-03-04T20:06:12.8301672Z + [[ dynamo_wrapped == distributed ]]
2025-03-04T20:06:12.8302123Z + [[ dynamo_wrapped == *inductor_distributed* ]]
2025-03-04T20:06:12.8302632Z + [[ dynamo_wrapped == *inductor-halide* ]]
2025-03-04T20:06:12.8303128Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]]
2025-03-04T20:06:12.8303572Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]]
2025-03-04T20:06:12.8303970Z + [[ dynamo_wrapped == *huggingface* ]]
2025-03-04T20:06:12.8304324Z + [[ dynamo_wrapped == *timm* ]]
2025-03-04T20:06:12.8304658Z + [[ dynamo_wrapped == cachebench ]]
2025-03-04T20:06:12.8305023Z + [[ dynamo_wrapped == verify_cachebench ]]
2025-03-04T20:06:12.8305395Z + [[ dynamo_wrapped == *torchbench* ]]
2025-03-04T20:06:12.8306040Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]]
2025-03-04T20:06:12.8306423Z + [[ dynamo_wrapped == *inductor* ]]
2025-03-04T20:06:12.8306792Z + [[ dynamo_wrapped == *dynamo_wrapped* ]]
2025-03-04T20:06:12.8307136Z + install_torchvision
2025-03-04T20:06:12.8307414Z + local orig_preload
2025-03-04T20:06:12.8307682Z + local commit
2025-03-04T20:06:12.8307955Z ++ get_pinned_commit vision
2025-03-04T20:06:12.8308275Z ++ cat .github/ci_commit_pins/vision.txt
2025-03-04T20:06:12.8325653Z + commit=d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:12.8326135Z + orig_preload=
2025-03-04T20:06:12.8326416Z + '[' -n '' ']'
2025-03-04T20:06:12.8327030Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:12.8327793Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T20:06:12.8328665Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:13.2084160Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:13.2088746Z   Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-puso1x01
2025-03-04T20:06:13.2113310Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-puso1x01
2025-03-04T20:06:14.7431377Z   Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f'
2025-03-04T20:06:14.7451942Z   Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:16.1539267Z   Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:16.4719947Z   Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T20:06:18.5062898Z   Preparing metadata (setup.py) ... [?25l- \ done
2025-03-04T20:06:18.5095671Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.1.2)
2025-03-04T20:06:18.5099093Z Requirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.7.0a0+git1b74980)
2025-03-04T20:06:18.5103494Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0)
2025-03-04T20:06:18.5168067Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1)
2025-03-04T20:06:18.5171539Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2)
2025-03-04T20:06:18.5182991Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.8.0)
2025-03-04T20:06:18.5186230Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3)
2025-03-04T20:06:18.5188875Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8)
2025-03-04T20:06:18.5191429Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.5)
2025-03-04T20:06:18.5194007Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0)
2025-03-04T20:06:18.5207469Z 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->torchvision==0.19.0a0+d23a6e1) (1.3.0)
2025-03-04T20:06:18.5311213Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2)
2025-03-04T20:06:18.5412078Z Building wheels for collected packages: torchvision
2025-03-04T20:07:28.4541099Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
2025-03-04T20:07:28.4579647Z [?25h  Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp313-cp313-linux_x86_64.whl size=1162213 sha256=59883d7e73540abaf26f7697de0d8f034cd2dc90fd7533628dc0efbaa4107a41
2025-03-04T20:07:28.4581039Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/81/60/73/f2acb628a45eebe28dd9ff5468e774a0d5e194728570f8ff6f
2025-03-04T20:07:28.4615872Z Successfully built torchvision
2025-03-04T20:07:28.5981807Z Installing collected packages: torchvision
2025-03-04T20:07:29.0482193Z Successfully installed torchvision-0.19.0a0+d23a6e1
2025-03-04T20:07:29.1682578Z + '[' -n '' ']'
2025-03-04T20:07:29.1682945Z + test_dynamo_wrapped_shard 1
2025-03-04T20:07:29.1683274Z + [[ -z 3 ]]
2025-03-04T20:07:29.1683557Z + python tools/dynamo/verify_dynamo.py
2025-03-04T20:07:30.3742128Z Python version: 3.13.2
2025-03-04T20:07:30.3742509Z `torch` version: 2.7.0a0+git1b74980
2025-03-04T20:07:30.3742857Z CUDA version: None
2025-03-04T20:07:30.3743128Z ROCM version: None
2025-03-04T20:07:30.3743311Z 
2025-03-04T20:07:30.3743866Z /var/lib/jenkins/workspace/tools/dynamo/verify_dynamo.py:220: UserWarning: Dynamo not yet supported in Python 3.13. Skipping check.
2025-03-04T20:07:30.3745270Z   warnings.warn("Dynamo not yet supported in Python 3.13. Skipping check.")
2025-03-04T20:07:30.3746053Z All required checks passed
2025-03-04T20:07:30.6386415Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --exclude-aot-dispatch-tests --shard 1 3 --verbose --upload-artifacts-while-running
2025-03-04T20:07:30.7431124Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-04T20:07:30.7432834Z   import pkg_resources
2025-03-04T20:07:35.3550719Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-04T20:07:35.4266587Z Ignoring disabled issues:  ['']
2025-03-04T20:07:35.4428907Z Found test times from artifacts
2025-03-04T20:07:35.5076563Z Found test times from artifacts
2025-03-04T20:07:35.5100633Z Running all tests
2025-03-04T20:07:35.5212929Z Running parallel tests on 3 processes
2025-03-04T20:07:35.5217576Z Name: tests to run (est. time: 54.55min)
2025-03-04T20:07:35.5218075Z   Serial tests (41):
2025-03-04T20:07:35.5218371Z     test_torch 1/1
2025-03-04T20:07:35.5218630Z     test_nn 2/2
2025-03-04T20:07:35.5218975Z     test_cpp_extensions_open_device_registration 1/1
2025-03-04T20:07:35.5219356Z     test_utils 1/1
2025-03-04T20:07:35.5219619Z     test_fake_tensor 1/1
2025-03-04T20:07:35.5219908Z     test_show_pickle 1/1
2025-03-04T20:07:35.5220191Z     test_multiprocessing 1/1
2025-03-04T20:07:35.5220496Z     test_dispatch 1/1
2025-03-04T20:07:35.5220769Z     test_autocast 1/1
2025-03-04T20:07:35.5221068Z     test_tensor_creation_ops 1/1
2025-03-04T20:07:35.5221458Z     test_cpp_extensions_jit 1/1
2025-03-04T20:07:35.5221773Z     test_native_mha 1/1
2025-03-04T20:07:35.5222118Z     nn/test_convolution 1/1
2025-03-04T20:07:35.5222584Z     test_sort_and_select 1/1
2025-03-04T20:07:35.5223073Z     test_multiprocessing_spawn 1/1
2025-03-04T20:07:35.5223698Z     nn/test_pooling 1/1
2025-03-04T20:07:35.5224006Z     test_mobile_optimizer 1/1
2025-03-04T20:07:35.5224305Z     test_fx 1/1
2025-03-04T20:07:35.5224587Z     test_spectral_ops 1/1
2025-03-04T20:07:35.5224884Z     test_python_dispatch 1/1
2025-03-04T20:07:35.5225463Z     distributions/test_distributions 1/2
2025-03-04T20:07:35.5225848Z     distributions/test_distributions 2/2
2025-03-04T20:07:35.5226201Z     test_tensorexpr 1/1
2025-03-04T20:07:35.5226494Z     test_namedtuple_return_api 1/1
2025-03-04T20:07:35.5226836Z     test_autograd_fallback 1/1
2025-03-04T20:07:35.5227150Z     test_jit_disabled 1/1
2025-03-04T20:07:35.5227463Z     test_cpp_extensions_aot_no_ninja 1/1
2025-03-04T20:07:35.5227825Z     test_cpp_extensions_aot_ninja 1/1
2025-03-04T20:07:35.5228153Z     test_cuda_trace 1/1
2025-03-04T20:07:35.5228436Z     test_cuda_primary_ctx 1/1
2025-03-04T20:07:35.5228746Z     test_reductions 1/1
2025-03-04T20:07:35.5229034Z     test_cuda_nvml_based_avail 1/1
2025-03-04T20:07:35.5229352Z     test_overrides 1/1
2025-03-04T20:07:35.5229647Z     test_transformers_privateuse1 1/1
2025-03-04T20:07:35.5229987Z     test_extension_utils 1/1
2025-03-04T20:07:35.5230298Z     test_ci_sanity_check_fail 1/1
2025-03-04T20:07:35.5230643Z     test_cpp_extensions_mtia_backend 1/1
2025-03-04T20:07:35.5231016Z     test_cpp_extensions_stream_and_event 1/1
2025-03-04T20:07:35.5231364Z     doctests 1/1
2025-03-04T20:07:35.5231617Z     test_autoload_disable 1/1
2025-03-04T20:07:35.5231930Z     test_autoload_enable 1/1
2025-03-04T20:07:35.5232230Z   Parallel tests (32):
2025-03-04T20:07:35.5232526Z     dynamo/test_graph_break_messages 1/1
2025-03-04T20:07:35.5232871Z     dynamo/test_export 1/1
2025-03-04T20:07:35.5233165Z     dynamo/test_repros 1/1
2025-03-04T20:07:35.5233465Z     dynamo/test_decorators 1/1
2025-03-04T20:07:35.5233778Z     dynamo/test_optimizers 1/1
2025-03-04T20:07:35.5234088Z     dynamo/test_minifier 1/1
2025-03-04T20:07:35.5234400Z     dynamo/test_backends 1/1
2025-03-04T20:07:35.5234709Z     dynamo/test_aot_autograd 1/1
2025-03-04T20:07:35.5235029Z     dynamo/test_functions 1/1
2025-03-04T20:07:35.5235345Z     dynamo/test_skip_non_tensor 1/1
2025-03-04T20:07:35.5235681Z     dynamo/test_pre_dispatch 1/1
2025-03-04T20:07:35.5236012Z     dynamo/test_python_autograd 1/1
2025-03-04T20:07:35.5236344Z     dynamo/test_exceptions 1/1
2025-03-04T20:07:35.5236661Z     dynamo/test_hooks 1/1
2025-03-04T20:07:35.5237000Z     dynamo/test_cudagraphs_expandable_segments 1/1
2025-03-04T20:07:35.5237387Z     dynamo/test_base_output 1/1
2025-03-04T20:07:35.5237704Z     dynamo/test_reconstruct 1/1
2025-03-04T20:07:35.5238014Z     dynamo/test_view 1/1
2025-03-04T20:07:35.5238306Z     dynamo/test_trace_rules 1/1
2025-03-04T20:07:35.5238613Z     dynamo/test_compile 1/1
2025-03-04T20:07:35.5238919Z     dynamo/test_deviceguard 1/1
2025-03-04T20:07:35.5239258Z     dynamo/test_backward_higher_order_ops 1/1
2025-03-04T20:07:35.5239754Z     dynamo/test_base_hop 1/1
2025-03-04T20:07:35.5240072Z     dynamo/test_bytecode_utils 1/1
2025-03-04T20:07:35.5240419Z     dynamo/test_aot_autograd_cache 1/1
2025-03-04T20:07:35.5240775Z     dynamo/test_input_attr_tracking 1/1
2025-03-04T20:07:35.5241116Z     test_jiterator 1/1
2025-03-04T20:07:35.5241400Z     test_jit_fuser_te 1/1
2025-03-04T20:07:35.5241707Z     test_appending_byte_serializer 1/1
2025-03-04T20:07:35.5242061Z     functorch/test_ac 1/1
2025-03-04T20:07:35.5242363Z     test_accelerator 1/1
2025-03-04T20:07:35.5242660Z     optim/test_optim 1/1
2025-03-04T20:07:35.5242965Z Name: excluded (est. time: 0.0min)
2025-03-04T20:07:35.5243289Z   Serial tests (0):
2025-03-04T20:07:35.5243564Z   Parallel tests (0):
2025-03-04T20:07:35.5324085Z Running test_torch 1/1 ... [2025-03-04 20:07:35.532023]
2025-03-04T20:07:35.5325033Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:07:35.5328871Z 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'] ... [2025-03-04 20:07:35.532472]
2025-03-04T20:12:05.5664740Z 
2025-03-04T20:12:05.5665615Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_7f8c4481062be502_.log
2025-03-04T20:12:05.5995991Z Running 1034 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_conj_physical_meta_stride, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_ressurecting_clear, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cuda_errors_with_cpu_scalars_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_64bit_indexing_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, 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, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_large_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu
2025-03-04T20:12:05.6319066Z 
2025-03-04T20:12:05.6319265Z Running test_nn 2/2 ... [2025-03-04 20:12:05.568496]
2025-03-04T20:12:05.6319673Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:12:05.6320706Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:12:05.568827]
2025-03-04T20:17:43.7542716Z 
2025-03-04T20:17:43.7543585Z test_nn 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_2.2_d0496998163d97f2_.log
2025-03-04T20:17:43.8179662Z Running 1137 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_reduce, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_CTCLoss_critical_target_len, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cpu, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cuda, test/test_nn.py::TestNN::test_CTCLoss_long_targets, test/test_nn.py::TestNN::test_CTCLoss_typechecks, test/test_nn.py::TestNN::test_CTCLoss_zero_infinity, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_max, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx_cuda, test/test_nn.py::TestNN::test_Embedding_cuda, test/test_nn.py::TestNN::test_Embedding_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, test/test_nn.py::TestNN::test_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Fold, test/test_nn.py::TestNN::test_Fold_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_HuberLoss_delta, test/test_nn.py::TestNN::test_HuberLoss_delta_cuda, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim, test/test_nn.py::TestNN::test_Linear, test/test_nn.py::TestNN::test_Linear_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args, test/test_nn.py::TestNN::test_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, test/test_nn.py::TestNN::test_RNN_change_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_no_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_with_dropout, test/test_nn.py::TestNN::test_RNN_cudnn_weight_norm, test/test_nn.py::TestNN::test_RNN_dropout, test/test_nn.py::TestNN::test_RNN_input_size_zero, test/test_nn.py::TestNN::test_RNN_nonlinearity, test/test_nn.py::TestNN::test_RNN_nonlinearity_passed_as_arg, test/test_nn.py::TestNN::test_RReLU, test/test_nn.py::TestNN::test_RReLU_with_up_down, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim, test/test_nn.py::TestNN::test_Sequential_add, test/test_nn.py::TestNN::test_Sequential_append, test/test_nn.py::TestNN::test_Sequential_delitem, test/test_nn.py::TestNN::test_Sequential_imul, test/test_nn.py::TestNN::test_Sequential_insert, test/test_nn.py::TestNN::test_Sequential_insert_fail_case, test/test_nn.py::TestNN::test_Sequential_mul, test/test_nn.py::TestNN::test_Sequential_pop, test/test_nn.py::TestNN::test_Sequential_setitem, test/test_nn.py::TestNN::test_Sequential_setitem_named, test/test_nn.py::TestNN::test_SiLU_no_batch_dim, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_Softplus_no_batch_dim, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softsign_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_add_module, test/test_nn.py::TestNN::test_affine_grid, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_always_nonnegative, test/test_nn.py::TestNN::test_bce_loss_broadcasts_weights, test/test_nn.py::TestNN::test_bce_loss_input_range, test/test_nn.py::TestNN::test_bce_loss_size_mismatch, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_pos_weights, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bce_with_logits_raises_if_target_and_input_are_different_size, test/test_nn.py::TestNN::test_bce_with_logits_stability, test/test_nn.py::TestNN::test_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_no_bias, test/test_nn.py::TestNN::test_bilinear_non_contiguous, test/test_nn.py::TestNN::test_broadcast_not_requiring_grad, test/test_nn.py::TestNN::test_buffer_not_persistent, test/test_nn.py::TestNN::test_buffer_not_persistent_assign, test/test_nn.py::TestNN::test_buffer_not_persistent_del, test/test_nn.py::TestNN::test_buffer_not_persistent_overwrite, test/test_nn.py::TestNN::test_call_supports_python_dict_output, test/test_nn.py::TestNN::test_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_children, test/test_nn.py::TestNN::test_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_diff_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_nonexpandable_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_invalid_shape, test/test_nn.py::TestNN::test_cosine_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_forward_exception, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, test/test_nn.py::TestNN::test_elu_inplace_on_view, test/test_nn.py::TestNN::test_error_RNN_seq_len_zero, test/test_nn.py::TestNN::test_extra_state_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fold_invalid_arg, test/test_nn.py::TestNN::test_get_buffer_from_submodules, test/test_nn.py::TestNN::test_getattr_with_property, test/test_nn.py::TestNN::test_grid_sample_nearest_neighbor_rounding_mode_consistency, test/test_nn.py::TestNN::test_interpolate, test/test_nn.py::TestNN::test_interpolate_bicubic_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_illegal_memory_access, test/test_nn.py::TestNN::test_interpolate_linear_1d, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d, test/test_nn.py::TestNN::test_interpolate_nearest_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d, test/test_nn.py::TestNN::test_kl_div_log_softmax_target, test/test_nn.py::TestNN::test_kl_div_with_diff_type, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_layer_norm_eps, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_broadcasting, test/test_nn.py::TestNN::test_linear_raise_on_scalar_input, test/test_nn.py::TestNN::test_log_softmax_dim0, test/test_nn.py::TestNN::test_log_softmax_dim0_cuda, test/test_nn.py::TestNN::test_log_softmax_dim3, test/test_nn.py::TestNN::test_log_softmax_dim3_cuda, test/test_nn.py::TestNN::test_log_softmax_lastdim, test/test_nn.py::TestNN::test_log_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_log_softmax_scalar_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial, test/test_nn.py::TestNN::test_log_softmax_spatial_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial_special, test/test_nn.py::TestNN::test_margin_ranking_loss_margin_no_reduce, test/test_nn.py::TestNN::test_max_pool1d_invalid_output_size, test/test_nn.py::TestNN::test_module_apply_inplace_op, test/test_nn.py::TestNN::test_module_to_argparse, test/test_nn.py::TestNN::test_mse_loss_size_warning, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_nn.py::TestNN::test_named_children, test/test_nn.py::TestNN::test_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_nested_tensor_from_mask_error, test/test_nn.py::TestNN::test_no_grad, test/test_nn.py::TestNN::test_non_leaf_parameters, test/test_nn.py::TestNN::test_normalize, test/test_nn.py::TestNN::test_pad_scalar_error, test/test_nn.py::TestNN::test_pairwise_distance, test/test_nn.py::TestNN::test_parameter_assignment, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameters_and_named_parameters, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_partial_flat_weights, test/test_nn.py::TestNN::test_pdist, test/test_nn.py::TestNN::test_pdist_cpu_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_lstm_check_device, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_not_tensor, test/test_nn.py::TestNN::test_register_parameter_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, test/test_nn.py::TestNN::test_softmax_functional_dim3_cuda, test/test_nn.py::TestNN::test_softmax_functional_scalar, test/test_nn.py::TestNN::test_softmax_functional_scalar_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_cuda, test/test_nn.py::TestNN::test_softmax_spatial_dtype, test/test_nn.py::TestNN::test_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_accuracy_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_backward_elemt, test/test_nn.py::TestNN::test_threshold_bfloat16_half, test/test_nn.py::TestNN::test_threshold_int, test/test_nn.py::TestNN::test_train_errors_for_invalid_mode, test/test_nn.py::TestNN::test_transformer_layer_args_check, test/test_nn.py::TestNN::test_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, test/test_nn.py::TestNN::test_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, test/test_nn.py::TestNN::test_vector_to_parameters, test/test_nn.py::TestNN::test_weight_norm, test/test_nn.py::TestNN::test_weight_norm_pickle, test/test_nn.py::TestNN::test_zero_grad, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_Bilinear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_empty_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LocalResponseNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_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_fails_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate45_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate90_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_update_stats_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_channel_shuffle_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_error_if_nonfinite_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_index_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_all_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_bfloat16_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_logsigmoid_out_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_lstmcell_backward_only_one_output_grad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_mask_types_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_mean_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_replicatepad_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_retain_variables_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_save_lstm_compatibility_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_silu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smoothl1loss_backward_zero_beta_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_smem_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_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_gelu_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32
2025-03-04T20:17:43.8799463Z 
2025-03-04T20:17:43.8799804Z Running test_cpp_extensions_open_device_registration 1/1 ... [2025-03-04 20:17:43.756443]
2025-03-04T20:17:45.4171555Z running install
2025-03-04T20:17:45.4185473Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:17:45.4186316Z !!
2025-03-04T20:17:45.4186489Z 
2025-03-04T20:17:45.4186628Z         ********************************************************************************
2025-03-04T20:17:45.4187041Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:17:45.4187471Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:17:45.4187869Z         standards-based tools.
2025-03-04T20:17:45.4188078Z 
2025-03-04T20:17:45.4188407Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:17:45.4188959Z         ********************************************************************************
2025-03-04T20:17:45.4189221Z 
2025-03-04T20:17:45.4189310Z !!
2025-03-04T20:17:45.4189554Z   self.initialize_options()
2025-03-04T20:17:45.4317252Z running build
2025-03-04T20:17:45.4317529Z running build_py
2025-03-04T20:17:45.4400613Z creating build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:17:45.4421646Z copying pytorch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:17:45.4428778Z copying pytorch_openreg/_aten_impl.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:17:45.4436948Z copying pytorch_openreg/_device_daemon.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:17:45.4444518Z copying pytorch_openreg/_meta_parser.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:17:45.4455553Z running build_ext
2025-03-04T20:17:45.5946211Z building 'pytorch_openreg._C' extension
2025-03-04T20:17:45.5949418Z creating /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc
2025-03-04T20:17:45.6308016Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:17:45.6309071Z Compiling objects...
2025-03-04T20:17:45.6309433Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:17:46.2428684Z [1/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:17:46.3201667Z [2/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:17:46.3433025Z [3/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:17:46.3485890Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so
2025-03-04T20:17:46.7064414Z running install_lib
2025-03-04T20:17:46.7143291Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-04T20:17:46.7185374Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:17:46.7192668Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:17:46.7194473Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_aten_impl.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:17:46.7196161Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_device_daemon.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:17:46.7197745Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_meta_parser.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:17:46.7199016Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:17:46.7249210Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/__init__.py to __init__.cpython-313.pyc
2025-03-04T20:17:46.7252855Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_aten_impl.py to _aten_impl.cpython-313.pyc
2025-03-04T20:17:46.7269725Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_device_daemon.py to _device_daemon.cpython-313.pyc
2025-03-04T20:17:46.7298328Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_meta_parser.py to _meta_parser.cpython-313.pyc
2025-03-04T20:17:46.7306781Z running install_egg_info
2025-03-04T20:17:46.7481696Z running egg_info
2025-03-04T20:17:46.7549428Z creating pytorch_openreg.egg-info
2025-03-04T20:17:46.7550279Z writing pytorch_openreg.egg-info/PKG-INFO
2025-03-04T20:17:46.7554175Z writing dependency_links to pytorch_openreg.egg-info/dependency_links.txt
2025-03-04T20:17:46.7556016Z writing requirements to pytorch_openreg.egg-info/requires.txt
2025-03-04T20:17:46.7557050Z writing top-level names to pytorch_openreg.egg-info/top_level.txt
2025-03-04T20:17:46.7558131Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-04T20:17:46.7633449Z reading manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-04T20:17:46.7640494Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-04T20:17:46.7642206Z Copying pytorch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info
2025-03-04T20:17:46.7647868Z running install_scripts
2025-03-04T20:17:47.2442972Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:17:47.2445955Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:17:47.244331]
2025-03-04T20:18:01.4785277Z 
2025-03-04T20:18:01.4787117Z test_cpp_extensions_open_device_registration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_open_device_registration_1.1_368e3adba1fdfc66_.log
2025-03-04T20:18:01.4804629Z Running 23 items in this shard: test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_base_device_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_common_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_aliasing, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_returns_self, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_cpu_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dispatchstub, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dlpack, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_faketensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_generator_registration_and_hooks, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_named_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_numpy_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_packed_sequence, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_quantized, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_random, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_scalar_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_pin_memory, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_resize, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_type, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensorlist_type_fallback
2025-03-04T20:18:01.4817545Z 
2025-03-04T20:18:01.4817797Z Running test_utils 1/1 ... [2025-03-04 20:18:01.478842]
2025-03-04T20:18:01.4818209Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:18:01.4819248Z 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'] ... [2025-03-04 20:18:01.479233]
2025-03-04T20:19:52.6091480Z 
2025-03-04T20:19:52.6092758Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_53a280d80a47fccb_.log
2025-03-04T20:19:52.8707691Z Running 6028 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, test/test_utils.py::TestONNXUtils::test_check_onnx_broadcast, test/test_utils.py::TestONNXUtils::test_prepare_onnx_paddings, test/test_utils.py::TestHipify::test_import_hipify, test/test_utils.py::TestHipifyTrie::test_add_and_search_trie, test/test_utils.py::TestHipifyTrie::test_add_multiple_and_search_trie, test/test_utils.py::TestHipifyTrie::test_char_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_prefix_words_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_quote_escape, test/test_utils.py::TestHipifyTrie::test_single_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_special_char_export_trie_to_regex, test/test_utils.py::TestAssert::test_assert_scriptable, test/test_utils.py::TestAssert::test_assert_true, test/test_utils.py::TestStandaloneCPPJIT::test_load_standalone, test/test_utils.py::TestRenderUtils::test_basic, test/test_utils.py::TestDeviceUtilsCPU::test_basic_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_generator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex64, 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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_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_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, 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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, 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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, 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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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short, test/test_utils.py::TestTryImport::test_import_existing, test/test_utils.py::TestTryImport::test_import_imported, test/test_utils.py::TestTryImport::test_import_missing
2025-03-04T20:19:53.0821341Z 
2025-03-04T20:19:53.0821626Z Running test_fake_tensor 1/1 ... [2025-03-04 20:19:52.616706]
2025-03-04T20:19:53.0822080Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:19:53.0823161Z 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'] ... [2025-03-04 20:19:52.617008]
2025-03-04T20:20:11.1567893Z 
2025-03-04T20:20:11.1569059Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_6c8859ddde3d10ac_.log
2025-03-04T20:20:11.1680951Z Running 268 items in this shard: test/test_fake_tensor.py::FakeTensorTest::test__adaptive_avg_pool2d_backward, test/test_fake_tensor.py::FakeTensorTest::test_alias_call, test/test_fake_tensor.py::FakeTensorTest::test_allow_meta, test/test_fake_tensor.py::FakeTensorTest::test_aten_copy_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_index_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_slice_scatter_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_basic, test/test_fake_tensor.py::FakeTensorTest::test_batch_tensor, test/test_fake_tensor.py::FakeTensorTest::test_binary_op_type_promotion, test/test_fake_tensor.py::FakeTensorTest::test_constructor, test/test_fake_tensor.py::FakeTensorTest::test_convert_fake_to_real, test/test_fake_tensor.py::FakeTensorTest::test_cpu_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cuda_initialized, test/test_fake_tensor.py::FakeTensorTest::test_cuda_lstm, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_with_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_without_fallback, test/test_fake_tensor.py::FakeTensorTest::test_custom_op_fallback, test/test_fake_tensor.py::FakeTensorTest::test_data_dependent_operator, test/test_fake_tensor.py::FakeTensorTest::test_deepcopy, test/test_fake_tensor.py::FakeTensorTest::test_device_inplace_copy, test/test_fake_tensor.py::FakeTensorTest::test_embedding_bag_meta, test/test_fake_tensor.py::FakeTensorTest::test_export_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fake_dispatch_keys, test/test_fake_tensor.py::FakeTensorTest::test_fake_grad_copy, test/test_fake_tensor.py::FakeTensorTest::test_fake_mode_error, test/test_fake_tensor.py::FakeTensorTest::test_from_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fsdp_flat_param, test/test_fake_tensor.py::FakeTensorTest::test_full, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex128, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int16, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int8, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_uint8, test/test_fake_tensor.py::FakeTensorTest::test_index_put_error, test/test_fake_tensor.py::FakeTensorTest::test_jagged_fake_to_fake_preserved, test/test_fake_tensor.py::FakeTensorTest::test_like_constructor, test/test_fake_tensor.py::FakeTensorTest::test_mixed_real_and_fake_inputs, test/test_fake_tensor.py::FakeTensorTest::test_mode, test/test_fake_tensor.py::FakeTensorTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_non_kwarg_device, test/test_fake_tensor.py::FakeTensorTest::test_non_overlapping_stride_zero, test/test_fake_tensor.py::FakeTensorTest::test_non_parameter_grad, test/test_fake_tensor.py::FakeTensorTest::test_normalize_device, test/test_fake_tensor.py::FakeTensorTest::test_out_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_parameter_instantiation, test/test_fake_tensor.py::FakeTensorTest::test_parameter_view, test/test_fake_tensor.py::FakeTensorTest::test_print_in_fake_mode, test/test_fake_tensor.py::FakeTensorTest::test_randperm, test/test_fake_tensor.py::FakeTensorTest::test_recursive_invocation, test/test_fake_tensor.py::FakeTensorTest::test_repr, test/test_fake_tensor.py::FakeTensorTest::test_same_shape_env_preserved, test/test_fake_tensor.py::FakeTensorTest::test_scalar_inputs, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_False, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_True, test/test_fake_tensor.py::FakeTensorTest::test_setitem, test/test_fake_tensor.py::FakeTensorTest::test_shape_take_not_device, test/test_fake_tensor.py::FakeTensorTest::test_split_return_self, test/test_fake_tensor.py::FakeTensorTest::test_throw, test/test_fake_tensor.py::FakeTensorTest::test_tolist, test/test_fake_tensor.py::FakeTensorTest::test_type_as, test/test_fake_tensor.py::FakeTensorTest::test_unsqueeze_copy, test/test_fake_tensor.py::FakeTensorTest::test_upsample_bilinear_small_channels, test/test_fake_tensor.py::FakeTensorTest::test_zero_dim, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test__adaptive_avg_pool2d_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_alias_call_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_allow_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_copy_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_index_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_slice_scatter_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_basic_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_batch_tensor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_binary_op_type_promotion_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_convert_fake_to_real_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cpu_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_initialized_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_lstm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_with_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_without_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_custom_op_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_data_dependent_operator_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_deepcopy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_device_inplace_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_embedding_bag_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_export_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_dispatch_keys_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_grad_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_from_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fsdp_flat_param_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_full_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex128_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int16_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_uint8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_put_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_jagged_fake_to_fake_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_like_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mixed_real_and_fake_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_overlapping_stride_zero_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_parameter_grad_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_normalize_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_out_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_instantiation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_print_in_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_randperm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_recursive_invocation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_repr_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_same_shape_env_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scalar_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_False_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_True_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_setitem_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_shape_take_not_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_split_return_self_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_throw_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_tolist_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_type_as_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unsqueeze_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_upsample_bilinear_small_channels_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_zero_dim_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorConstHandling::test_aliased_const_write, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_propagate_through_functions, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_batch_norm_cpu, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_in_intlist_repro, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_add, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_view_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storage_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storages, test/test_fake_tensor.py::FakeTensorConstHandling::test_simple, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_aliased_const_write_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_propagate_through_functions_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_batch_norm_cpu_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_in_intlist_repro_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_add_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_view_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storage_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storages_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_simple_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_key, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_weak_ref, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_from_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_to_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_multiple_modes, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_active_mode, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_ref_cycle, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_mode_error, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_non_view, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_view, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_key_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_weak_ref_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_from_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_to_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_multiple_modes_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_active_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_ref_cycle_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_non_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_view_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_conv_c1_backward, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_cross_entropy_loss, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_embedding_bag_private, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_fake_gpu_no_init, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_flash_attention, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_like_ops, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_no_dispatch_with_like_function, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_non_kwarg_only_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_sparse_new, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_str_storage, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_new, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_conv_c1_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_cross_entropy_loss_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_embedding_bag_private_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_fake_gpu_no_init_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_flash_attention_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_like_ops_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_no_dispatch_with_like_function_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_non_kwarg_only_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_sparse_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_str_storage_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_new_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args, test/test_fake_tensor.py::FakeTensorPropTest::test_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_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_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_meta_tensor_to_fake_cpu, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device
2025-03-04T20:20:11.1789033Z 
2025-03-04T20:20:11.1789238Z Running test_show_pickle 1/1 ... [2025-03-04 20:20:11.157385]
2025-03-04T20:20:11.1789677Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:20:11.1790761Z 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'] ... [2025-03-04 20:20:11.157730]
2025-03-04T20:20:15.0275807Z 
2025-03-04T20:20:15.0277147Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_da5dda921c105935_.log
2025-03-04T20:20:15.0278812Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model
2025-03-04T20:20:15.0279550Z 
2025-03-04T20:20:15.0280457Z Running test_multiprocessing 1/1 ... [2025-03-04 20:20:15.027855]
2025-03-04T20:20:15.0281131Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:20:15.0285862Z 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'] ... [2025-03-04 20:20:15.028267]
2025-03-04T20:21:32.2027010Z 
2025-03-04T20:21:32.2028018Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_a6209014cfc7f374_.log
2025-03-04T20:21:32.2042851Z Running 41 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_meta_simple, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork
2025-03-04T20:21:32.2056658Z 
2025-03-04T20:21:32.2056864Z Running test_dispatch 1/1 ... [2025-03-04 20:21:32.202958]
2025-03-04T20:21:32.2057285Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:21:32.2058416Z 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'] ... [2025-03-04 20:21:32.203278]
2025-03-04T20:22:27.4427049Z 
2025-03-04T20:22:27.4428314Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_0677422d8715a6d8_.log
2025-03-04T20:22:27.4441656Z 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
2025-03-04T20:22:27.4451728Z 
2025-03-04T20:22:27.4451946Z Running test_autocast 1/1 ... [2025-03-04 20:22:27.443008]
2025-03-04T20:22:27.4452373Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:22:27.4453428Z 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'] ... [2025-03-04 20:22:27.443343]
2025-03-04T20:22:41.9772617Z 
2025-03-04T20:22:41.9774110Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_f5f882e2d87b9790_.log
2025-03-04T20:22:41.9781418Z 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
2025-03-04T20:22:41.9787753Z 
2025-03-04T20:22:41.9788005Z Running test_tensor_creation_ops 1/1 ... [2025-03-04 20:22:41.977506]
2025-03-04T20:22:41.9788490Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:22:41.9789588Z 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'] ... [2025-03-04 20:22:41.977823]
2025-03-04T20:26:10.0214380Z 
2025-03-04T20:26:10.0215798Z 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_bf1f9f481cfa9b6c_.log
2025-03-04T20:26:10.0468639Z Running 640 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_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_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
2025-03-04T20:26:10.0704708Z 
2025-03-04T20:26:10.0704961Z Running test_cpp_extensions_jit 1/1 ... [2025-03-04 20:26:10.023402]
2025-03-04T20:26:10.0705616Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:26:10.0706724Z 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'] ... [2025-03-04 20:26:10.023795]
2025-03-04T20:26:46.7395968Z 
2025-03-04T20:26:46.7397403Z 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_4c48089d03fe3b89_.log
2025-03-04T20:26:46.7413199Z Running 29 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_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_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
2025-03-04T20:26:46.7424772Z 
2025-03-04T20:26:46.7424989Z Running test_native_mha 1/1 ... [2025-03-04 20:26:46.740013]
2025-03-04T20:26:46.7425420Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:26:46.7426472Z 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'] ... [2025-03-04 20:26:46.740415]
2025-03-04T20:27:11.8430073Z 
2025-03-04T20:27:11.8431070Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_4f85d64063ee6d76_.log
2025-03-04T20:27:11.8454236Z 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
2025-03-04T20:27:11.8475299Z 
2025-03-04T20:27:11.8475505Z Running nn/test_convolution 1/1 ... [2025-03-04 20:27:11.843218]
2025-03-04T20:27:11.8475954Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:27:11.8477131Z 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'] ... [2025-03-04 20:27:11.843522]
2025-03-04T20:31:01.0321736Z 
2025-03-04T20:31:01.0322633Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_a58d9e000db84c83_.log
2025-03-04T20:31:01.0666872Z Running 588 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_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float16, 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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_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
2025-03-04T20:31:01.1319895Z 
2025-03-04T20:31:01.1320286Z Running test_sort_and_select 1/1 ... [2025-03-04 20:31:01.033410]
2025-03-04T20:31:01.1321119Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:31:01.1323186Z 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'] ... [2025-03-04 20:31:01.033706]
2025-03-04T20:32:03.8838220Z 
2025-03-04T20:32:03.8839335Z 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_b77f713deefffcc0_.log
2025-03-04T20:32:03.8881130Z 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_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_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
2025-03-04T20:32:03.8919685Z 
2025-03-04T20:32:03.8919932Z Running test_multiprocessing_spawn 1/1 ... [2025-03-04 20:32:03.884411]
2025-03-04T20:32:03.8920415Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:32:03.8921532Z 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'] ... [2025-03-04 20:32:03.884732]
2025-03-04T20:34:27.1436388Z 
2025-03-04T20:34:27.1437937Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_670c320406c3949f_.log
2025-03-04T20:34:27.1453155Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable
2025-03-04T20:34:27.1464484Z 
2025-03-04T20:34:27.1464700Z Running nn/test_pooling 1/1 ... [2025-03-04 20:34:27.143847]
2025-03-04T20:34:27.1465135Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:34:27.1466198Z 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'] ... [2025-03-04 20:34:27.144238]
2025-03-04T20:35:26.1053228Z 
2025-03-04T20:35:26.1054207Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_c57660566d7072b3_.log
2025-03-04T20:35:26.1100190Z Running 104 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_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_backward_fails_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_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu
2025-03-04T20:35:26.1139904Z 
2025-03-04T20:35:26.1140217Z Running test_mobile_optimizer 1/1 ... [2025-03-04 20:35:26.105858]
2025-03-04T20:35:26.1140679Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:35:26.1141842Z 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'] ... [2025-03-04 20:35:26.106264]
2025-03-04T20:35:35.9340845Z 
2025-03-04T20:35:35.9341980Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_ce88d1d82250f324_.log
2025-03-04T20:35:35.9345495Z 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
2025-03-04T20:35:35.9348197Z 
2025-03-04T20:35:35.9348373Z Running test_fx 1/1 ... [2025-03-04 20:35:35.934194]
2025-03-04T20:35:35.9348787Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:35:35.9349801Z 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'] ... [2025-03-04 20:35:35.934511]
2025-03-04T20:38:40.0404918Z 
2025-03-04T20:38:40.0405724Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_98a6977ddc6c4c72_.log
2025-03-04T20:38:40.0901588Z Running 1257 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_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_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_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_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, 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test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2
2025-03-04T20:38:40.1373251Z 
2025-03-04T20:38:40.1373509Z Running test_spectral_ops 1/1 ... [2025-03-04 20:38:40.043034]
2025-03-04T20:38:40.1374154Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:38:40.1375270Z 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'] ... [2025-03-04 20:38:40.043454]
2025-03-04T20:39:12.1012893Z 
2025-03-04T20:39:12.1014107Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_d012086a966f0ae3_.log
2025-03-04T20:39:12.1108556Z 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, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_float32, 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test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_ifft_rfft_irfft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_input_modification_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_invalid_dtypes_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_plan_repeatable_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_int8, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_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
2025-03-04T20:39:12.1200714Z 
2025-03-04T20:39:12.1200930Z Running test_python_dispatch 1/1 ... [2025-03-04 20:39:12.101914]
2025-03-04T20:39:12.1201386Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:39:12.1202471Z 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'] ... [2025-03-04 20:39:12.102222]
2025-03-04T20:39:27.6870577Z 
2025-03-04T20:39:27.6871726Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_517b036717830741_.log
2025-03-04T20:39:27.6916462Z Running 114 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_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_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_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_autograd_device_check, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_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
2025-03-04T20:39:27.6959763Z 
2025-03-04T20:39:27.6960049Z Running distributions/test_distributions 1/2 ... [2025-03-04 20:39:27.687386]
2025-03-04T20:39:27.6960626Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:39:27.6961788Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:39:27.687701]
2025-03-04T20:44:31.6047300Z 
2025-03-04T20:44:31.6048686Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_9d863c3aea73c2e0_.log
2025-03-04T20:44:31.6104430Z Running 130 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, test/distributions/test_distributions.py::TestDistributions::test_wishart_moments, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_zero_excluded_binomial, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_alpha, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, test/distributions/test_distributions.py::TestDistributionShapes::test_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_sample
2025-03-04T20:44:31.6156019Z 
2025-03-04T20:44:31.6156309Z Running distributions/test_distributions 2/2 ... [2025-03-04 20:44:31.605066]
2025-03-04T20:44:31.6156829Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:44:31.6158064Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:44:31.605384]
2025-03-04T20:50:26.7427244Z 
2025-03-04T20:50:26.7428463Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_2e4d1e983b5f5d46_.log
2025-03-04T20:50:26.7470757Z Running 96 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_sequential_draw, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance
2025-03-04T20:50:26.7509559Z 
2025-03-04T20:50:26.7509761Z Running test_tensorexpr 1/1 ... [2025-03-04 20:50:26.742986]
2025-03-04T20:50:26.7510201Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:50:26.7511264Z 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'] ... [2025-03-04 20:50:26.743294]
2025-03-04T20:50:30.2124016Z 
2025-03-04T20:50:30.2125432Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_92b1692a81dba73f_.log
2025-03-04T20:50:30.2150821Z 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
2025-03-04T20:50:30.2171031Z 
2025-03-04T20:50:30.2171308Z Running test_namedtuple_return_api 1/1 ... [2025-03-04 20:50:30.212738]
2025-03-04T20:50:30.2171846Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:50:30.2172950Z 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'] ... [2025-03-04 20:50:30.213135]
2025-03-04T20:50:35.6859725Z 
2025-03-04T20:50:35.6860763Z 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_250cd7dc33aa8298_.log
2025-03-04T20:50:35.6862819Z 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
2025-03-04T20:50:35.6864194Z 
2025-03-04T20:50:35.6864429Z Running test_autograd_fallback 1/1 ... [2025-03-04 20:50:35.686102]
2025-03-04T20:50:35.6864992Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:50:35.6866668Z 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'] ... [2025-03-04 20:50:35.686429]
2025-03-04T20:50:41.1579636Z 
2025-03-04T20:50:41.1580674Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_3551f7f7147ae718_.log
2025-03-04T20:50:41.1594526Z 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
2025-03-04T20:50:41.1607629Z 
2025-03-04T20:50:41.1607864Z Running test_jit_disabled 1/1 ... [2025-03-04 20:50:41.158173]
2025-03-04T20:50:41.1608298Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:50:41.1609483Z 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'] ... [2025-03-04 20:50:41.158484]
2025-03-04T20:50:44.8778980Z 
2025-03-04T20:50:44.8780112Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_d0da3c03a68982e7_.log
2025-03-04T20:50:44.8781713Z 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
2025-03-04T20:50:44.8782832Z 
2025-03-04T20:50:44.8783088Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-03-04 20:50:44.878091]
2025-03-04T20:50:47.4922940Z running install
2025-03-04T20:50:47.4924424Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:50:47.4925250Z !!
2025-03-04T20:50:47.4925662Z 
2025-03-04T20:50:47.4925799Z         ********************************************************************************
2025-03-04T20:50:47.4926210Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:50:47.4926714Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:50:47.4927111Z         standards-based tools.
2025-03-04T20:50:47.4927322Z 
2025-03-04T20:50:47.4927636Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:50:47.4928261Z         ********************************************************************************
2025-03-04T20:50:47.4928540Z 
2025-03-04T20:50:47.4928631Z !!
2025-03-04T20:50:47.4928875Z   self.initialize_options()
2025-03-04T20:50:47.5058790Z running build
2025-03-04T20:50:47.5059284Z running build_py
2025-03-04T20:50:47.5147430Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:50:47.5149848Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:50:47.5161254Z running build_ext
2025-03-04T20:50:47.5945505Z building 'torch_test_cpp_extension.cpp' extension
2025-03-04T20:50:47.5946283Z creating build/temp.linux-x86_64-cpython-313
2025-03-04T20:50:47.5951246Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:50:48.7695645Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2025-03-04T20:50:48.7696681Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2025-03-04T20:50:48.7697573Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9,
2025-03-04T20:50:48.7698228Z                  from extension.cpp:1:
2025-03-04T20:50:48.7779936Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2025-03-04T20:50:48.7781101Z extension.cpp:45:53:   required from here
2025-03-04T20:50:48.7782713Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2025-03-04T20:50:48.7784065Z  1539 | class class_ : public detail::generic_type {
2025-03-04T20:50:48.7784440Z       |       ^~~~~~
2025-03-04T20:50:48.7786091Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2025-03-04T20:50:48.7787497Z extension.cpp:45:53:   required from here
2025-03-04T20:50:48.7790880Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2025-03-04T20:50:48.7793610Z  1599 |             with_internals([&](internals &internals) {
2025-03-04T20:50:48.7794011Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:50:48.7794555Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2025-03-04T20:50:48.7795248Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:50:48.7795731Z  1601 |                                                       : internals.registered_types_cpp;
2025-03-04T20:50:48.7796167Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:50:48.7796613Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2025-03-04T20:50:48.7797053Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:50:48.7797475Z  1603 |                     = instances[std::type_index(typeid(type))];
2025-03-04T20:50:48.7797890Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:50:48.7798241Z  1604 |             });
2025-03-04T20:50:48.7798575Z       |             ~               
2025-03-04T20:50:48.7801830Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:50:49.1812025Z building 'torch_test_cpp_extension.maia' extension
2025-03-04T20:50:49.1817223Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:50:50.2554520Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:50:50.6462640Z building 'torch_test_cpp_extension.rng' extension
2025-03-04T20:50:50.6467233Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:50:51.9676733Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:50:51.9678329Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:50:51.9679688Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:50:51.9681292Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:50:51.9682112Z                  from rng_extension.cpp:6:
2025-03-04T20:50:51.9682953Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1155: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:50:51.9683785Z  1155 | # pragma unroll
2025-03-04T20:50:51.9684056Z       | 
2025-03-04T20:50:51.9684623Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1195,
2025-03-04T20:50:51.9685572Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:50:51.9686580Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:50:51.9687402Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:50:51.9688331Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:50:51.9689027Z                  from rng_extension.cpp:6:
2025-03-04T20:50:51.9689833Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:50:51.9690627Z    59 | #pragma unroll
2025-03-04T20:50:51.9690894Z       | 
2025-03-04T20:50:51.9691589Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:50:51.9692583Z    72 | #pragma unroll
2025-03-04T20:50:51.9692856Z       | 
2025-03-04T20:50:51.9693564Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:50:51.9694360Z    87 | #pragma unroll
2025-03-04T20:50:51.9694628Z       | 
2025-03-04T20:50:51.9695187Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1196,
2025-03-04T20:50:51.9696124Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:50:51.9696962Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:50:51.9697844Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:50:51.9698785Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:50:51.9699483Z                  from rng_extension.cpp:6:
2025-03-04T20:50:51.9700427Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:50:51.9701249Z   153 | #pragma unroll
2025-03-04T20:50:51.9701515Z       | 
2025-03-04T20:50:51.9704762Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:50:52.3833641Z running install_lib
2025-03-04T20:50:52.3911680Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-04T20:50:52.3915008Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:50:52.3916450Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:50:52.3918224Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:50:52.4009001Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:50:52.4095034Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:50:52.4190816Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-313.pyc
2025-03-04T20:50:52.4193218Z running install_egg_info
2025-03-04T20:50:52.4369011Z running egg_info
2025-03-04T20:50:52.4438482Z creating torch_test_cpp_extension.egg-info
2025-03-04T20:50:52.4439549Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-04T20:50:52.4442886Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-04T20:50:52.4444721Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-04T20:50:52.4446826Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-04T20:50:52.4448125Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:50:52.4524551Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:50:52.4531792Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:50:52.4533294Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-04T20:50:52.4539401Z running install_scripts
2025-03-04T20:50:54.6754340Z running install
2025-03-04T20:50:54.6755395Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:50:54.6756236Z !!
2025-03-04T20:50:54.6756362Z 
2025-03-04T20:50:54.6756508Z         ********************************************************************************
2025-03-04T20:50:54.6756960Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:50:54.6757399Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:50:54.6757803Z         standards-based tools.
2025-03-04T20:50:54.6758001Z 
2025-03-04T20:50:54.6758330Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:50:54.6759151Z         ********************************************************************************
2025-03-04T20:50:54.6759411Z 
2025-03-04T20:50:54.6759516Z !!
2025-03-04T20:50:54.6759823Z   self.initialize_options()
2025-03-04T20:50:54.6889040Z running build
2025-03-04T20:50:54.6889303Z running build_ext
2025-03-04T20:50:54.7963416Z building 'no_python_abi_suffix_test' extension
2025-03-04T20:50:54.7966122Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313
2025-03-04T20:50:54.8270654Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:50:54.8271857Z Compiling objects...
2025-03-04T20:50:54.8272208Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:50:54.9234079Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:50:54.9281176Z creating build/lib.linux-x86_64-cpython-313
2025-03-04T20:50:54.9286261Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so
2025-03-04T20:50:54.9865876Z running install_lib
2025-03-04T20:50:54.9944165Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-04T20:50:54.9947563Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-04T20:50:54.9952361Z running install_egg_info
2025-03-04T20:50:55.0126586Z running egg_info
2025-03-04T20:50:55.0194327Z creating no_python_abi_suffix_test.egg-info
2025-03-04T20:50:55.0195496Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2025-03-04T20:50:55.0198625Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2025-03-04T20:50:55.0201131Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2025-03-04T20:50:55.0201861Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-04T20:50:55.0272936Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-04T20:50:55.0279823Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-04T20:50:55.0281463Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info
2025-03-04T20:50:55.0286023Z running install_scripts
2025-03-04T20:50:55.4852426Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:50:55.4855383Z 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'] ... [2025-03-04 20:50:55.485254]
2025-03-04T20:51:01.5208854Z 
2025-03-04T20:51:01.5210187Z 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_d678bd4c3fd820d4_.log
2025-03-04T20:51:01.5218301Z Running 19 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_no_ninja.py::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_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library
2025-03-04T20:51:01.5225137Z 
2025-03-04T20:51:01.5225403Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-03-04 20:51:01.521090]
2025-03-04T20:51:04.2029096Z running install
2025-03-04T20:51:04.2030378Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:51:04.2031582Z !!
2025-03-04T20:51:04.2031709Z 
2025-03-04T20:51:04.2031855Z         ********************************************************************************
2025-03-04T20:51:04.2032262Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:51:04.2032692Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:51:04.2033097Z         standards-based tools.
2025-03-04T20:51:04.2033294Z 
2025-03-04T20:51:04.2033621Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:51:04.2034177Z         ********************************************************************************
2025-03-04T20:51:04.2034429Z 
2025-03-04T20:51:04.2034535Z !!
2025-03-04T20:51:04.2034787Z   self.initialize_options()
2025-03-04T20:51:04.2162632Z running build
2025-03-04T20:51:04.2162893Z running build_py
2025-03-04T20:51:04.2242049Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:51:04.2243954Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:51:04.2248095Z running build_ext
2025-03-04T20:51:04.3340023Z building 'torch_test_cpp_extension.cpp' extension
2025-03-04T20:51:04.3341737Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313
2025-03-04T20:51:04.3643149Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:51:04.3643988Z Compiling objects...
2025-03-04T20:51:04.3644349Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:51:05.0592305Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:51:05.0692890Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:51:05.3324208Z building 'torch_test_cpp_extension.maia' extension
2025-03-04T20:51:05.3623512Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:51:05.3645464Z Compiling objects...
2025-03-04T20:51:05.3646128Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:51:06.0433379Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:51:06.0487442Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:51:06.3046413Z building 'torch_test_cpp_extension.rng' extension
2025-03-04T20:51:06.3350881Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:51:06.3352113Z Compiling objects...
2025-03-04T20:51:06.3352492Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:51:07.2229807Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:51:07.2283909Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:51:07.5063462Z running install_lib
2025-03-04T20:51:07.5144724Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:51:07.5190869Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:51:07.5239437Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:51:07.5292338Z running install_egg_info
2025-03-04T20:51:07.5471269Z running egg_info
2025-03-04T20:51:07.5542073Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-04T20:51:07.5545646Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-04T20:51:07.5557814Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-04T20:51:07.5568019Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-04T20:51:07.5653119Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:51:07.5661906Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:51:07.5672658Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-04T20:51:07.5674839Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-04T20:51:07.5681088Z running install_scripts
2025-03-04T20:51:09.8224404Z running install
2025-03-04T20:51:09.8225820Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:51:09.8226668Z !!
2025-03-04T20:51:09.8226808Z 
2025-03-04T20:51:09.8226942Z         ********************************************************************************
2025-03-04T20:51:09.8227359Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:51:09.8227817Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:51:09.8228228Z         standards-based tools.
2025-03-04T20:51:09.8228441Z 
2025-03-04T20:51:09.8228761Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:51:09.8229329Z         ********************************************************************************
2025-03-04T20:51:09.8229595Z 
2025-03-04T20:51:09.8229689Z !!
2025-03-04T20:51:09.8229933Z   self.initialize_options()
2025-03-04T20:51:09.8356488Z running build
2025-03-04T20:51:09.8356824Z running build_ext
2025-03-04T20:51:09.9438138Z building 'no_python_abi_suffix_test' extension
2025-03-04T20:51:09.9741915Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:51:09.9742850Z Compiling objects...
2025-03-04T20:51:09.9743202Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:51:10.0000402Z ninja: no work to do.
2025-03-04T20:51:10.0045262Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so
2025-03-04T20:51:10.0627040Z running install_lib
2025-03-04T20:51:10.0707609Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages
2025-03-04T20:51:10.0712862Z running install_egg_info
2025-03-04T20:51:10.0891394Z running egg_info
2025-03-04T20:51:10.0961094Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2025-03-04T20:51:10.0964997Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2025-03-04T20:51:10.0977467Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2025-03-04T20:51:10.1058766Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-04T20:51:10.1066798Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2025-03-04T20:51:10.1077372Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-04T20:51:10.1079134Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info
2025-03-04T20:51:10.1084571Z running install_scripts
2025-03-04T20:51:10.5757838Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:51:10.5760822Z 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'] ... [2025-03-04 20:51:10.575836]
2025-03-04T20:51:16.6034628Z 
2025-03-04T20:51:16.6035645Z 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_be605a22e147c789_.log
2025-03-04T20:51:16.6043243Z Running 19 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_ninja.py::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_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library
2025-03-04T20:51:16.6049954Z 
2025-03-04T20:51:16.6050163Z Running test_cuda_trace 1/1 ... [2025-03-04 20:51:16.603700]
2025-03-04T20:51:16.6050619Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:51:16.6051727Z 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'] ... [2025-03-04 20:51:16.604034]
2025-03-04T20:51:19.9515554Z 
2025-03-04T20:51:19.9516453Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_87971e0343fda7c0_.log
2025-03-04T20:51:19.9517213Z Running 0 items in this shard:
2025-03-04T20:51:19.9517675Z 
2025-03-04T20:51:19.9518995Z Running test_cuda_primary_ctx 1/1 ... [2025-03-04 20:51:19.951735]
2025-03-04T20:51:19.9519488Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:51:19.9523265Z 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'] ... [2025-03-04 20:51:19.952067]
2025-03-04T20:51:23.2984516Z 
2025-03-04T20:51:23.2985480Z 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_0057f4cee823fd7e_.log
2025-03-04T20:51:23.2986299Z Running 0 items in this shard:
2025-03-04T20:51:23.2986525Z 
2025-03-04T20:51:23.2987937Z Running test_reductions 1/1 ... [2025-03-04 20:51:23.298630]
2025-03-04T20:51:23.2988399Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:51:23.2991796Z 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'] ... [2025-03-04 20:51:23.298957]
2025-03-04T20:57:06.3771320Z 
2025-03-04T20:57:06.3772665Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_baa43e515275452f_.log
2025-03-04T20:57:06.5504826Z Running 4565 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, 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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
2025-03-04T20:57:06.7191957Z 
2025-03-04T20:57:06.7192222Z Running test_cuda_nvml_based_avail 1/1 ... [2025-03-04 20:57:06.383492]
2025-03-04T20:57:06.7192714Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:57:06.7193892Z 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'] ... [2025-03-04 20:57:06.383812]
2025-03-04T20:57:09.7495809Z 
2025-03-04T20:57:09.7496756Z 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_ab3cf9a3f1e38fc7_.log
2025-03-04T20:57:09.7497610Z Running 0 items in this shard:
2025-03-04T20:57:09.7497905Z 
2025-03-04T20:57:09.7499334Z Running test_overrides 1/1 ... [2025-03-04 20:57:09.749749]
2025-03-04T20:57:09.7499763Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:57:09.7502741Z 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'] ... [2025-03-04 20:57:09.750075]
2025-03-04T20:58:20.7570671Z 
2025-03-04T20:58:20.7571614Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_1b4bed757a5c9c35_.log
2025-03-04T20:58:20.8066998Z Running 1467 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma_, 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, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, 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_warn_on_invalid_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_tensor_subclass, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_device_context_semantics, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_torch_function_ctx, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_torch_function_all_disabled_api, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes
2025-03-04T20:58:20.8548962Z 
2025-03-04T20:58:20.8549247Z Running test_transformers_privateuse1 1/1 ... [2025-03-04 20:58:20.759011]
2025-03-04T20:58:22.2351720Z running install
2025-03-04T20:58:22.2353234Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:58:22.2354331Z !!
2025-03-04T20:58:22.2354467Z 
2025-03-04T20:58:22.2354602Z         ********************************************************************************
2025-03-04T20:58:22.2355028Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:58:22.2355480Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:58:22.2355882Z         standards-based tools.
2025-03-04T20:58:22.2356092Z 
2025-03-04T20:58:22.2356411Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:58:22.2356977Z         ********************************************************************************
2025-03-04T20:58:22.2357242Z 
2025-03-04T20:58:22.2357333Z !!
2025-03-04T20:58:22.2357576Z   self.initialize_options()
2025-03-04T20:58:22.2483639Z running build
2025-03-04T20:58:22.2483900Z running build_py
2025-03-04T20:58:22.2560685Z creating build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:58:22.2562931Z copying pytorch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:58:22.2565106Z copying pytorch_openreg/_aten_impl.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:58:22.2567146Z copying pytorch_openreg/_device_daemon.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:58:22.2568703Z copying pytorch_openreg/_meta_parser.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg
2025-03-04T20:58:22.2574847Z running build_ext
2025-03-04T20:58:22.3644793Z building 'pytorch_openreg._C' extension
2025-03-04T20:58:22.3648252Z creating /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc
2025-03-04T20:58:22.3950652Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/build.ninja...
2025-03-04T20:58:22.3951663Z Compiling objects...
2025-03-04T20:58:22.3952019Z Using envvar MAX_JOBS (6) as the number of workers...
2025-03-04T20:58:22.7268989Z [1/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:58:22.7497314Z [2/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:58:22.7814723Z [3/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:58:22.7870440Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so
2025-03-04T20:58:23.0690635Z running install_lib
2025-03-04T20:58:23.0770749Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg
2025-03-04T20:58:23.0827136Z running install_egg_info
2025-03-04T20:58:23.0997182Z running egg_info
2025-03-04T20:58:23.1065311Z writing pytorch_openreg.egg-info/PKG-INFO
2025-03-04T20:58:23.1069187Z writing dependency_links to pytorch_openreg.egg-info/dependency_links.txt
2025-03-04T20:58:23.1071401Z writing requirements to pytorch_openreg.egg-info/requires.txt
2025-03-04T20:58:23.1072580Z writing top-level names to pytorch_openreg.egg-info/top_level.txt
2025-03-04T20:58:23.1148657Z reading manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-04T20:58:23.1158034Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt'
2025-03-04T20:58:23.1159573Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info' (and everything under it)
2025-03-04T20:58:23.1161357Z Copying pytorch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info
2025-03-04T20:58:23.1167711Z running install_scripts
2025-03-04T20:58:23.5843262Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:58:23.5846260Z 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'] ... [2025-03-04 20:58:23.584318]
2025-03-04T20:58:31.8106038Z 
2025-03-04T20:58:31.8107356Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_533f1e2fa652d6fb_.log
2025-03-04T20:58:31.8110326Z 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
2025-03-04T20:58:31.8112438Z 
2025-03-04T20:58:31.8112847Z Running test_extension_utils 1/1 ... [2025-03-04 20:58:31.810773]
2025-03-04T20:58:31.8113286Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:58:31.8114377Z 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'] ... [2025-03-04 20:58:31.811078]
2025-03-04T20:58:35.7306998Z 
2025-03-04T20:58:35.7308179Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_910d2c7af651b230_.log
2025-03-04T20:58:35.7310079Z 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
2025-03-04T20:58:35.7311539Z 
2025-03-04T20:58:35.7311775Z Running test_ci_sanity_check_fail 1/1 ... [2025-03-04 20:58:35.730890]
2025-03-04T20:58:35.7312255Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:58:35.7314750Z 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'] ... [2025-03-04 20:58:35.731203]
2025-03-04T20:58:47.1788256Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-03-04 20:58:47.178436]
2025-03-04T20:58:47.1788810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:58:47.1804243Z 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'] ... [2025-03-04 20:58:47.178769]
2025-03-04T20:58:51.2988415Z 
2025-03-04T20:58:51.2990043Z 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_c9ed471cad2a366f_.log
2025-03-04T20:58:51.2993569Z 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
2025-03-04T20:58:51.2996324Z 
2025-03-04T20:58:51.2996628Z Running test_cpp_extensions_stream_and_event 1/1 ... [2025-03-04 20:58:51.298999]
2025-03-04T20:58:51.2997151Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:58:51.2998320Z 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'] ... [2025-03-04 20:58:51.299319]
2025-03-04T20:58:55.3192189Z 
2025-03-04T20:58:55.3193552Z 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_e5bcde3bef77b45a_.log
2025-03-04T20:58:55.3195296Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event
2025-03-04T20:58:55.3195891Z 
2025-03-04T20:58:55.3196061Z Running doctests 1/1 ... [2025-03-04 20:58:55.319256]
2025-03-04T20:58:55.4259504Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch')
2025-03-04T20:58:55.4260064Z Listing tests
2025-03-04T20:58:55.7284223Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1507.
2025-03-04T20:58:55.7285190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.7285584Z 
2025-03-04T20:58:55.7285742Z dim_order(ambiguity_check=False) -> tuple
2025-03-04T20:58:55.7285985Z 
2025-03-04T20:58:55.7286263Z Returns the uniquely determined tuple of int describing the dim order or
2025-03-04T20:58:55.7286755Z physical layout of :attr:`self`.
2025-03-04T20:58:55.7286964Z 
2025-03-04T20:58:55.7287243Z The dim order represents how dimensions are laid out in memory of dense tensors,
2025-03-04T20:58:55.7287814Z starting from the outermost to the innermost dimension.
2025-03-04T20:58:55.7288105Z 
2025-03-04T20:58:55.7288314Z Note that the dim order may not always be uniquely determined.
2025-03-04T20:58:55.7289277Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
2025-03-04T20:58:55.7290222Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted
2025-03-04T20:58:55.7291024Z into exactly one of the given memory formats, or it cannot be uniquely determined.
2025-03-04T20:58:55.7291744Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness.
2025-03-04T20:58:55.7292329Z Otherwise, it will raise TypeError.
2025-03-04T20:58:55.7292551Z 
2025-03-04T20:58:55.7292656Z Args:
2025-03-04T20:58:55.7293121Z     ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order.
2025-03-04T20:58:55.7293570Z 
2025-03-04T20:58:55.7293714Z Examples::
2025-03-04T20:58:55.7293851Z 
2025-03-04T20:58:55.7293987Z     >>> torch.empty((2, 3, 5, 7)).dim_order()
2025-03-04T20:58:55.7294321Z     (0, 1, 2, 3)
2025-03-04T20:58:55.7294631Z     >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order()
2025-03-04T20:58:55.7295477Z     (0, 2, 1, 3)
2025-03-04T20:58:55.7295847Z     >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order()
2025-03-04T20:58:55.7296282Z     (0, 2, 3, 1)
2025-03-04T20:58:55.7296560Z     >>> torch.empty((1, 2, 3, 4)).dim_order()
2025-03-04T20:58:55.7297006Z     (0, 1, 2, 3)
2025-03-04T20:58:55.7297255Z     >>> try:
2025-03-04T20:58:55.7297580Z     ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True)
2025-03-04T20:58:55.7298088Z     ... except RuntimeError as e:
2025-03-04T20:58:55.7298408Z     ...     print(e)
2025-03-04T20:58:55.7298902Z     The tensor does not have unique dim order, or cannot map to exact one of the given memory formats.
2025-03-04T20:58:55.7299476Z     >>> torch.empty((1, 2, 3, 4)).dim_order(
2025-03-04T20:58:55.7299935Z     ...     ambiguity_check=[torch.contiguous_format, torch.channels_last]
2025-03-04T20:58:55.7300424Z     ... )  # It can be mapped to contiguous format
2025-03-04T20:58:55.7300772Z     (0, 1, 2, 3)
2025-03-04T20:58:55.7301016Z     >>> try:
2025-03-04T20:58:55.7301355Z     ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL")
2025-03-04T20:58:55.7301784Z     ... except TypeError as e:
2025-03-04T20:58:55.7302073Z     ...     print(e)
2025-03-04T20:58:55.7302491Z     The ambiguity_check argument must be a bool or a list of memory formats.
2025-03-04T20:58:55.7302860Z 
2025-03-04T20:58:55.7302964Z .. warning::
2025-03-04T20:58:55.7303322Z     The dim_order tensor API is experimental and subject to change.
2025-03-04T20:58:55.7303650Z 
2025-03-04T20:58:55.7303912Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.7304299Z 
2025-03-04T20:58:55.7822185Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=432.
2025-03-04T20:58:55.7823165Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.7823835Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2025-03-04T20:58:55.7824207Z 
2025-03-04T20:58:55.7824406Z         This is helpful when you want to visualize data over some
2025-03-04T20:58:55.7824878Z         range of inputs. See below for a plotting example.
2025-03-04T20:58:55.7825152Z 
2025-03-04T20:58:55.7825354Z         Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2025-03-04T20:58:55.7825846Z         inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2025-03-04T20:58:55.7826366Z         this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2025-03-04T20:58:55.7826859Z         G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2025-03-04T20:58:55.7827329Z         the output :math:`G_i` is constructed by expanding :math:`T_i`
2025-03-04T20:58:55.7827765Z         to the result shape.
2025-03-04T20:58:55.7827973Z 
2025-03-04T20:58:55.7828099Z         .. note::
2025-03-04T20:58:55.7828625Z             0D inputs are treated equivalently to 1D inputs of a
2025-03-04T20:58:55.7829029Z             single element.
2025-03-04T20:58:55.7829232Z 
2025-03-04T20:58:55.7829333Z         .. warning::
2025-03-04T20:58:55.7829698Z             `torch.meshgrid(*tensors)` currently has the same behavior
2025-03-04T20:58:55.7830191Z             as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2025-03-04T20:58:55.7830489Z 
2025-03-04T20:58:55.7830651Z             In the future `torch.meshgrid` will transition to
2025-03-04T20:58:55.7831054Z             `indexing='xy'` as the default.
2025-03-04T20:58:55.7831299Z 
2025-03-04T20:58:55.7831500Z             https://github.com/pytorch/pytorch/issues/50276 tracks
2025-03-04T20:58:55.7832005Z             this issue with the goal of migrating to NumPy's behavior.
2025-03-04T20:58:55.7832326Z 
2025-03-04T20:58:55.7832432Z         .. seealso::
2025-03-04T20:58:55.7832604Z 
2025-03-04T20:58:55.7832786Z             :func:`torch.cartesian_prod` has the same effect but it
2025-03-04T20:58:55.7833229Z             collects the data in a tensor of vectors.
2025-03-04T20:58:55.7833492Z 
2025-03-04T20:58:55.7833657Z         Args:
2025-03-04T20:58:55.7834075Z             tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2025-03-04T20:58:55.7834747Z                 treated as tensors of size :math:`(1,)` automatically
2025-03-04T20:58:55.7835048Z 
2025-03-04T20:58:55.7835241Z             indexing: (str, optional): the indexing mode, either "xy"
2025-03-04T20:58:55.7835726Z                 or "ij", defaults to "ij". See warning for future changes.
2025-03-04T20:58:55.7836032Z 
2025-03-04T20:58:55.7836194Z                 If "xy" is selected, the first dimension corresponds
2025-03-04T20:58:55.7836651Z                 to the cardinality of the second input and the second
2025-03-04T20:58:55.7837131Z                 dimension corresponds to the cardinality of the first
2025-03-04T20:58:55.7837532Z                 input.
2025-03-04T20:58:55.7837709Z 
2025-03-04T20:58:55.7837867Z                 If "ij" is selected, the dimensions are in the same
2025-03-04T20:58:55.7838289Z                 order as the cardinality of the inputs.
2025-03-04T20:58:55.7838685Z 
2025-03-04T20:58:55.7838824Z         Returns:
2025-03-04T20:58:55.7839292Z             seq (sequence of Tensors): If the input has :math:`N`
2025-03-04T20:58:55.7839973Z             tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2025-03-04T20:58:55.7840617Z             output will also have :math:`N` tensors, where each tensor
2025-03-04T20:58:55.7841181Z             is of shape :math:`(S_0, ..., S_{N-1})`.
2025-03-04T20:58:55.7841427Z 
2025-03-04T20:58:55.7841553Z         Example::
2025-03-04T20:58:55.7841704Z 
2025-03-04T20:58:55.7841854Z             >>> x = torch.tensor([1, 2, 3])
2025-03-04T20:58:55.7842243Z             >>> y = torch.tensor([4, 5, 6])
2025-03-04T20:58:55.7842470Z 
2025-03-04T20:58:55.7842766Z             Observe the element-wise pairings across the grid, (1, 4),
2025-03-04T20:58:55.7843240Z             (1, 5), ..., (3, 6). This is the same thing as the
2025-03-04T20:58:55.7843620Z             cartesian product.
2025-03-04T20:58:55.7844000Z             >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2025-03-04T20:58:55.7844390Z             >>> grid_x
2025-03-04T20:58:55.7844653Z             tensor([[1, 1, 1],
2025-03-04T20:58:55.7844950Z                     [2, 2, 2],
2025-03-04T20:58:55.7845236Z                     [3, 3, 3]])
2025-03-04T20:58:55.7845522Z             >>> grid_y
2025-03-04T20:58:55.7845786Z             tensor([[4, 5, 6],
2025-03-04T20:58:55.7846076Z                     [4, 5, 6],
2025-03-04T20:58:55.7846359Z                     [4, 5, 6]])
2025-03-04T20:58:55.7846559Z 
2025-03-04T20:58:55.7846737Z             This correspondence can be seen when these grids are
2025-03-04T20:58:55.7847176Z             stacked properly.
2025-03-04T20:58:55.7847584Z             >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2025-03-04T20:58:55.7848126Z             ...             torch.cartesian_prod(x, y))
2025-03-04T20:58:55.7848483Z             True
2025-03-04T20:58:55.7848631Z 
2025-03-04T20:58:55.7848834Z             `torch.meshgrid` is commonly used to produce a grid for
2025-03-04T20:58:55.7849243Z             plotting.
2025-03-04T20:58:55.7849553Z             >>> # xdoctest: +REQUIRES(module:matplotlib)
2025-03-04T20:58:55.7849951Z             >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2025-03-04T20:58:55.7850340Z             >>> import matplotlib.pyplot as plt
2025-03-04T20:58:55.7850727Z             >>> xs = torch.linspace(-5, 5, steps=100)
2025-03-04T20:58:55.7851106Z             >>> ys = torch.linspace(-5, 5, steps=100)
2025-03-04T20:58:55.7851498Z             >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2025-03-04T20:58:55.7851884Z             >>> z = torch.sin(torch.sqrt(x * x + y * y))
2025-03-04T20:58:55.7852265Z             >>> ax = plt.axes(projection='3d')
2025-03-04T20:58:55.7852668Z             >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2025-03-04T20:58:55.7853055Z             >>> plt.show()
2025-03-04T20:58:55.7853243Z 
2025-03-04T20:58:55.7853434Z         .. image:: ../_static/img/meshgrid.png
2025-03-04T20:58:55.7853794Z             :width: 512
2025-03-04T20:58:55.7853976Z 
2025-03-04T20:58:55.7854068Z         
2025-03-04T20:58:55.7854545Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.7854936Z 
2025-03-04T20:58:55.7855518Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=828.
2025-03-04T20:58:55.7856411Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.7857200Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
2025-03-04T20:58:55.7857702Z 
2025-03-04T20:58:55.7857953Z     Returns the unique elements of the input tensor.
2025-03-04T20:58:55.7858224Z 
2025-03-04T20:58:55.7858584Z     .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2025-03-04T20:58:55.7859231Z         this function also eliminates non-consecutive duplicate values.
2025-03-04T20:58:55.7859564Z 
2025-03-04T20:58:55.7859816Z     .. note:: Currently in the CUDA implementation and the CPU implementation,
2025-03-04T20:58:55.7860484Z         `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2025-03-04T20:58:55.7861198Z         Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2025-03-04T20:58:55.7861803Z         :func:`torch.unique_consecutive` which avoids the sorting.
2025-03-04T20:58:55.7862110Z 
2025-03-04T20:58:55.7862217Z     Args:
2025-03-04T20:58:55.7862475Z         input (Tensor): the input tensor
2025-03-04T20:58:55.7862934Z         sorted (bool): Whether to sort the unique elements in ascending order
2025-03-04T20:58:55.7863410Z             before returning as output.
2025-03-04T20:58:55.7863865Z         return_inverse (bool): Whether to also return the indices for where
2025-03-04T20:58:55.7864438Z             elements in the original input ended up in the returned unique list.
2025-03-04T20:58:55.7865023Z         return_counts (bool): Whether to also return the counts for each unique
2025-03-04T20:58:55.7865477Z             element.
2025-03-04T20:58:55.7865865Z         dim (int, optional): the dimension to operate upon. If ``None``, the
2025-03-04T20:58:55.7866421Z             unique of the flattened input is returned. Otherwise, each of the
2025-03-04T20:58:55.7866973Z             tensors indexed by the given dimension is treated as one of the
2025-03-04T20:58:55.7867540Z             elements to apply the unique operation upon. See examples for more
2025-03-04T20:58:55.7868008Z             details. Default: ``None``
2025-03-04T20:58:55.7868228Z 
2025-03-04T20:58:55.7868344Z     Returns:
2025-03-04T20:58:55.7868790Z         (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2025-03-04T20:58:55.7869237Z 
2025-03-04T20:58:55.7869457Z             - **output** (*Tensor*): the output list of unique scalar elements.
2025-03-04T20:58:55.7869939Z             - **inverse_indices** (*Tensor*): (optional) if
2025-03-04T20:58:55.7870399Z               :attr:`return_inverse` is True, there will be an additional
2025-03-04T20:58:55.7870933Z               returned tensor (same shape as input) representing the indices
2025-03-04T20:58:55.7871480Z               for where elements in the original input map to in the output;
2025-03-04T20:58:55.7871993Z               otherwise, this function will only return a single tensor.
2025-03-04T20:58:55.7872447Z             - **counts** (*Tensor*): (optional) if
2025-03-04T20:58:55.7872881Z               :attr:`return_counts` is True, there will be an additional
2025-03-04T20:58:55.7873382Z               returned tensor (same shape as output or output.size(dim),
2025-03-04T20:58:55.7874107Z               if dim was specified) representing the number of occurrences
2025-03-04T20:58:55.7874557Z               for each unique value or tensor.
2025-03-04T20:58:55.7874891Z 
2025-03-04T20:58:55.7874998Z     Example::
2025-03-04T20:58:55.7875152Z 
2025-03-04T20:58:55.7875375Z         >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2025-03-04T20:58:55.7875818Z         >>> output
2025-03-04T20:58:55.7876188Z         tensor([1, 2, 3])
2025-03-04T20:58:55.7876381Z 
2025-03-04T20:58:55.7876519Z         >>> output, inverse_indices = torch.unique(
2025-03-04T20:58:55.7877020Z         ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-04T20:58:55.7877494Z         >>> output
2025-03-04T20:58:55.7877754Z         tensor([1, 2, 3])
2025-03-04T20:58:55.7878081Z         >>> inverse_indices
2025-03-04T20:58:55.7878378Z         tensor([0, 2, 1, 2])
2025-03-04T20:58:55.7878561Z 
2025-03-04T20:58:55.7878710Z         >>> output, inverse_indices = torch.unique(
2025-03-04T20:58:55.7879213Z         ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-04T20:58:55.7879679Z         >>> output
2025-03-04T20:58:55.7879936Z         tensor([1, 2, 3])
2025-03-04T20:58:55.7880222Z         >>> inverse_indices
2025-03-04T20:58:55.7880508Z         tensor([[0, 2],
2025-03-04T20:58:55.7880760Z                 [1, 2]])
2025-03-04T20:58:55.7880941Z 
2025-03-04T20:58:55.7881049Z         >>> a = torch.tensor([
2025-03-04T20:58:55.7881342Z         ...     [
2025-03-04T20:58:55.7881596Z         ...         [1, 1, 0, 0],
2025-03-04T20:58:55.7881897Z         ...         [1, 1, 0, 0],
2025-03-04T20:58:55.7882196Z         ...         [0, 0, 1, 1],
2025-03-04T20:58:55.7882488Z         ...     ],
2025-03-04T20:58:55.7882735Z         ...     [
2025-03-04T20:58:55.7882985Z         ...         [0, 0, 1, 1],
2025-03-04T20:58:55.7883283Z         ...         [0, 0, 1, 1],
2025-03-04T20:58:55.7883577Z         ...         [1, 1, 1, 1],
2025-03-04T20:58:55.7883864Z         ...     ],
2025-03-04T20:58:55.7884112Z         ...     [
2025-03-04T20:58:55.7884359Z         ...         [1, 1, 0, 0],
2025-03-04T20:58:55.7884655Z         ...         [1, 1, 0, 0],
2025-03-04T20:58:55.7884953Z         ...         [0, 0, 1, 1],
2025-03-04T20:58:55.7885228Z         ...     ],
2025-03-04T20:58:55.7885471Z         ... ])
2025-03-04T20:58:55.7885621Z 
2025-03-04T20:58:55.7885847Z         >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2025-03-04T20:58:55.7886410Z         >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2025-03-04T20:58:55.7886893Z         >>> # each other, so one of them will be removed.
2025-03-04T20:58:55.7887264Z         >>> (a[0, :, :] == a[2, :, :]).all()
2025-03-04T20:58:55.7887586Z         tensor(True)
2025-03-04T20:58:55.7887883Z         >>> a_unique_dim0 = torch.unique(a, dim=0)
2025-03-04T20:58:55.7888235Z         >>> a_unique_dim0
2025-03-04T20:58:55.7888518Z         tensor([[[0, 0, 1, 1],
2025-03-04T20:58:55.7888880Z                  [0, 0, 1, 1],
2025-03-04T20:58:55.7889168Z                  [1, 1, 1, 1]],
2025-03-04T20:58:55.7889457Z                 [[1, 1, 0, 0],
2025-03-04T20:58:55.7889747Z                  [1, 1, 0, 0],
2025-03-04T20:58:55.7890037Z                  [0, 0, 1, 1]]])
2025-03-04T20:58:55.7890223Z 
2025-03-04T20:58:55.7890464Z         >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2025-03-04T20:58:55.7890916Z         >>> # `a_unique_dim0`:
2025-03-04T20:58:55.7891244Z         >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2025-03-04T20:58:55.7891590Z         tensor(True)
2025-03-04T20:58:55.7891877Z         >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2025-03-04T20:58:55.7892224Z         tensor(True)
2025-03-04T20:58:55.7892392Z 
2025-03-04T20:58:55.7892610Z         >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2025-03-04T20:58:55.7893145Z         >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2025-03-04T20:58:55.7893582Z         >>> # them will be removed.
2025-03-04T20:58:55.7893911Z         >>> (a[:, 0, :] == a[:, 1, :]).all()
2025-03-04T20:58:55.7894269Z         tensor(True)
2025-03-04T20:58:55.7894548Z         >>> torch.unique(a, dim=1)
2025-03-04T20:58:55.7894868Z         tensor([[[0, 0, 1, 1],
2025-03-04T20:58:55.7895156Z                  [1, 1, 0, 0]],
2025-03-04T20:58:55.7895505Z                 [[1, 1, 1, 1],
2025-03-04T20:58:55.7895794Z                  [0, 0, 1, 1]],
2025-03-04T20:58:55.7896084Z                 [[0, 0, 1, 1],
2025-03-04T20:58:55.7896378Z                  [1, 1, 0, 0]]])
2025-03-04T20:58:55.7896569Z 
2025-03-04T20:58:55.7896802Z         >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2025-03-04T20:58:55.7897324Z         >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2025-03-04T20:58:55.7897877Z         >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2025-03-04T20:58:55.7898313Z         >>> # sub-tensors will be removed.
2025-03-04T20:58:55.7898671Z         >>> (a[:, :, 0] == a[:, :, 1]).all()
2025-03-04T20:58:55.7898998Z         tensor(True)
2025-03-04T20:58:55.7899267Z         >>> (a[:, :, 2] == a[:, :, 3]).all()
2025-03-04T20:58:55.7899602Z         tensor(True)
2025-03-04T20:58:55.7899885Z         >>> torch.unique(a, dim=2)
2025-03-04T20:58:55.7900205Z         tensor([[[0, 1],
2025-03-04T20:58:55.7900511Z                  [0, 1],
2025-03-04T20:58:55.7900786Z                  [1, 0]],
2025-03-04T20:58:55.7901064Z                 [[1, 0],
2025-03-04T20:58:55.7901335Z                  [1, 0],
2025-03-04T20:58:55.7901602Z                  [1, 1]],
2025-03-04T20:58:55.7901874Z                 [[0, 1],
2025-03-04T20:58:55.7902139Z                  [0, 1],
2025-03-04T20:58:55.7902406Z                  [1, 0]]])
2025-03-04T20:58:55.7902674Z     
2025-03-04T20:58:55.7903061Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.7903438Z 
2025-03-04T20:58:55.8010370Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560.
2025-03-04T20:58:55.8011274Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8011677Z 
2025-03-04T20:58:55.8011907Z Load a model from a github repo or a local directory.
2025-03-04T20:58:55.8012224Z 
2025-03-04T20:58:55.8012569Z Note: Loading a model is the typical use case, but this can also be used to
2025-03-04T20:58:55.8013215Z for loading other objects such as tokenizers, loss functions, etc.
2025-03-04T20:58:55.8013552Z 
2025-03-04T20:58:55.8013743Z If ``source`` is 'github', ``repo_or_dir`` is expected to be
2025-03-04T20:58:55.8014215Z of the form ``repo_owner/repo_name[:ref]`` with an optional
2025-03-04T20:58:55.8014621Z ref (a tag or a branch).
2025-03-04T20:58:55.8014798Z 
2025-03-04T20:58:55.8014988Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2025-03-04T20:58:55.8015388Z path to a local directory.
2025-03-04T20:58:55.8015725Z 
2025-03-04T20:58:55.8015822Z Args:
2025-03-04T20:58:55.8016194Z     repo_or_dir (str): If ``source`` is 'github',
2025-03-04T20:58:55.8016769Z         this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2025-03-04T20:58:55.8017568Z         an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2025-03-04T20:58:55.8018395Z         the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2025-03-04T20:58:55.8019000Z         If ``source`` is 'local'  then it should be a path to a local directory.
2025-03-04T20:58:55.8019536Z     model (str): the name of a callable (entrypoint) defined in the
2025-03-04T20:58:55.8019978Z         repo/dir's ``hubconf.py``.
2025-03-04T20:58:55.8020433Z     *args (optional): the corresponding args for callable ``model``.
2025-03-04T20:58:55.8021103Z     source (str, optional): 'github' or 'local'. Specifies how
2025-03-04T20:58:55.8021672Z         ``repo_or_dir`` is to be interpreted. Default is 'github'.
2025-03-04T20:58:55.8022203Z     trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2025-03-04T20:58:55.8022924Z         This parameter was introduced in v1.12 and helps ensuring that users
2025-03-04T20:58:55.8023490Z         only run code from repos that they trust.
2025-03-04T20:58:55.8023765Z 
2025-03-04T20:58:55.8024112Z         - If ``False``, a prompt will ask the user whether the repo should
2025-03-04T20:58:55.8024568Z           be trusted.
2025-03-04T20:58:55.8024969Z         - If ``True``, the repo will be added to the trusted list and loaded
2025-03-04T20:58:55.8025489Z           without requiring explicit confirmation.
2025-03-04T20:58:55.8025936Z         - If ``"check"``, the repo will be checked against the list of
2025-03-04T20:58:55.8026515Z           trusted repos in the cache. If it is not present in that list, the
2025-03-04T20:58:55.8027111Z           behaviour will fall back onto the ``trust_repo=False`` option.
2025-03-04T20:58:55.8027674Z         - If ``None``: this will raise a warning, inviting the user to set
2025-03-04T20:58:55.8028199Z           ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2025-03-04T20:58:55.8028794Z           is only present for backward compatibility and will be removed in
2025-03-04T20:58:55.8029278Z           v2.0.
2025-03-04T20:58:55.8029422Z 
2025-03-04T20:58:55.8029656Z         Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2025-03-04T20:58:55.8030273Z     force_reload (bool, optional): whether to force a fresh download of
2025-03-04T20:58:55.8030867Z         the github repo unconditionally. Does not have any effect if
2025-03-04T20:58:55.8031320Z         ``source = 'local'``. Default is ``False``.
2025-03-04T20:58:55.8031845Z     verbose (bool, optional): If ``False``, mute messages about hitting
2025-03-04T20:58:55.8032453Z         local caches. Note that the message about first download cannot be
2025-03-04T20:58:55.8032983Z         muted. Does not have any effect if ``source = 'local'``.
2025-03-04T20:58:55.8033427Z         Default is ``True``.
2025-03-04T20:58:55.8033981Z     skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2025-03-04T20:58:55.8034763Z         specified by the ``github`` argument properly belongs to the repo owner. This will make
2025-03-04T20:58:55.8035508Z         requests to the GitHub API; you can specify a non-default GitHub token by setting the
2025-03-04T20:58:55.8036156Z         ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2025-03-04T20:58:55.8036695Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-04T20:58:55.8037108Z 
2025-03-04T20:58:55.8037204Z Returns:
2025-03-04T20:58:55.8037545Z     The output of the ``model`` callable when called with the given
2025-03-04T20:58:55.8038013Z     ``*args`` and ``**kwargs``.
2025-03-04T20:58:55.8038218Z 
2025-03-04T20:58:55.8038333Z Example:
2025-03-04T20:58:55.8038649Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-04T20:58:55.8039089Z     >>> # from a github repo
2025-03-04T20:58:55.8039435Z     >>> repo = "pytorch/vision"
2025-03-04T20:58:55.8039753Z     >>> model = torch.hub.load(
2025-03-04T20:58:55.8040209Z     ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2025-03-04T20:58:55.8040635Z     ... )
2025-03-04T20:58:55.8040915Z     >>> # from a local directory
2025-03-04T20:58:55.8041257Z     >>> path = "/some/local/path/pytorch/vision"
2025-03-04T20:58:55.8041678Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:55.8042119Z     >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2025-03-04T20:58:55.8042540Z 
2025-03-04T20:58:55.8042814Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8043249Z 
2025-03-04T20:58:55.8043784Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=652.
2025-03-04T20:58:55.8044694Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8045130Z 
2025-03-04T20:58:55.8045378Z Load a model from a local directory with a ``hubconf.py``.
2025-03-04T20:58:55.8045716Z 
2025-03-04T20:58:55.8045826Z Args:
2025-03-04T20:58:55.8046212Z     hubconf_dir (str): path to a local directory that contains a
2025-03-04T20:58:55.8046636Z         ``hubconf.py``.
2025-03-04T20:58:55.8047130Z     model (str): name of an entrypoint defined in the directory's
2025-03-04T20:58:55.8047540Z         ``hubconf.py``.
2025-03-04T20:58:55.8047982Z     *args (optional): the corresponding args for callable ``model``.
2025-03-04T20:58:55.8048542Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-04T20:58:55.8048898Z 
2025-03-04T20:58:55.8048993Z Returns:
2025-03-04T20:58:55.8049367Z     a single model with corresponding pretrained weights.
2025-03-04T20:58:55.8049671Z 
2025-03-04T20:58:55.8049765Z Example:
2025-03-04T20:58:55.8050030Z     >>> # xdoctest: +SKIP("stub local path")
2025-03-04T20:58:55.8050413Z     >>> path = "/some/local/path/pytorch/vision"
2025-03-04T20:58:55.8050779Z     >>> model = _load_local(
2025-03-04T20:58:55.8051070Z     ...     path,
2025-03-04T20:58:55.8051380Z     ...     "resnet50",
2025-03-04T20:58:55.8051699Z     ...     weights="ResNet50_Weights.IMAGENET1K_V1",
2025-03-04T20:58:55.8052057Z     ... )
2025-03-04T20:58:55.8052188Z 
2025-03-04T20:58:55.8052462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8052837Z 
2025-03-04T20:58:55.8053338Z 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=691.
2025-03-04T20:58:55.8054219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8054771Z Download object at the given URL to a local path.
2025-03-04T20:58:55.8055074Z 
2025-03-04T20:58:55.8055191Z     Args:
2025-03-04T20:58:55.8055460Z         url (str): URL of the object to download
2025-03-04T20:58:55.8055960Z         dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2025-03-04T20:58:55.8056673Z         hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2025-03-04T20:58:55.8057242Z             Default: None
2025-03-04T20:58:55.8057690Z         progress (bool, optional): whether or not to display a progress bar to stderr
2025-03-04T20:58:55.8058254Z             Default: True
2025-03-04T20:58:55.8058431Z 
2025-03-04T20:58:55.8058542Z     Example:
2025-03-04T20:58:55.8058822Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-04T20:58:55.8059210Z         >>> # xdoctest: +REQUIRES(POSIX)
2025-03-04T20:58:55.8059576Z         >>> torch.hub.download_url_to_file(
2025-03-04T20:58:55.8060056Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2025-03-04T20:58:55.8060522Z         ...     "/tmp/temporary_file",
2025-03-04T20:58:55.8060841Z         ... )
2025-03-04T20:58:55.8061043Z 
2025-03-04T20:58:55.8061134Z     
2025-03-04T20:58:55.8061523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8061916Z 
2025-03-04T20:58:55.8062501Z 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=816.
2025-03-04T20:58:55.8063404Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8063962Z Loads the Torch serialized object at the given URL.
2025-03-04T20:58:55.8064253Z 
2025-03-04T20:58:55.8064445Z     If downloaded file is a zip file, it will be automatically
2025-03-04T20:58:55.8064861Z     decompressed.
2025-03-04T20:58:55.8065031Z 
2025-03-04T20:58:55.8065260Z     If the object is already present in `model_dir`, it's deserialized and
2025-03-04T20:58:55.8065711Z     returned.
2025-03-04T20:58:55.8066080Z     The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2025-03-04T20:58:55.8066629Z     ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2025-03-04T20:58:55.8066959Z 
2025-03-04T20:58:55.8067050Z     Args:
2025-03-04T20:58:55.8067394Z         url (str): URL of the object to download
2025-03-04T20:58:55.8067854Z         model_dir (str, optional): directory in which to save the object
2025-03-04T20:58:55.8068605Z         map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2025-03-04T20:58:55.8069353Z         progress (bool, optional): whether or not to display a progress bar to stderr.
2025-03-04T20:58:55.8069849Z             Default: True
2025-03-04T20:58:55.8070367Z         check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2025-03-04T20:58:55.8071059Z             ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2025-03-04T20:58:55.8071649Z             digits of the SHA256 hash of the contents of the file. The hash is used to
2025-03-04T20:58:55.8072208Z             ensure unique names and to verify the contents of the file.
2025-03-04T20:58:55.8072632Z             Default: False
2025-03-04T20:58:55.8073161Z         file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2025-03-04T20:58:55.8074200Z         weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2025-03-04T20:58:55.8074930Z             Recommended for untrusted sources. See :func:`~torch.load` for more details.
2025-03-04T20:58:55.8075322Z 
2025-03-04T20:58:55.8075484Z     Example:
2025-03-04T20:58:55.8075775Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-04T20:58:55.8076185Z         >>> state_dict = torch.hub.load_state_dict_from_url(
2025-03-04T20:58:55.8076692Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2025-03-04T20:58:55.8077131Z         ... )
2025-03-04T20:58:55.8077281Z 
2025-03-04T20:58:55.8077369Z     
2025-03-04T20:58:55.8077761Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8078147Z 
2025-03-04T20:58:55.8101469Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=376.
2025-03-04T20:58:55.8102440Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:58:55.8103078Z Registers the function implementation as the fallback for the given key.
2025-03-04T20:58:55.8103454Z 
2025-03-04T20:58:55.8103681Z         This function only works for a library with global namespace ("_").
2025-03-04T20:58:55.8104037Z 
2025-03-04T20:58:55.8104133Z         Args:
2025-03-04T20:58:55.8104572Z             fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2025-03-04T20:58:55.8105110Z                 to register a fallthrough.
2025-03-04T20:58:55.8105682Z             dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2025-03-04T20:58:55.8106422Z                           the dispatch key that the library was created with.
2025-03-04T20:58:55.8107106Z             with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2025-03-04T20:58:55.8107946Z                          to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2025-03-04T20:58:55.8108407Z 
2025-03-04T20:58:55.8108535Z         Example::
2025-03-04T20:58:55.8108810Z             >>> my_lib = Library("_", "IMPL")
2025-03-04T20:58:55.8109189Z             >>> def fallback_kernel(op, *args, **kwargs):
2025-03-04T20:58:55.8109587Z             >>>     # Handle all autocast ops generically
2025-03-04T20:58:55.8109944Z             >>>     # ...
2025-03-04T20:58:55.8110281Z             >>> my_lib.fallback(fallback_kernel, "Autocast")
2025-03-04T20:58:55.8110650Z         
2025-03-04T20:58:55.8111423Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2025-03-04T20:58:55.8112229Z 
2025-03-04T20:58:55.8112382Z my_lib.fallback(fallback_kernel, "Autocast")
2025-03-04T20:58:55.8112737Z ^
2025-03-04T20:58:55.8181295Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=920.
2025-03-04T20:58:55.8182169Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:58:55.8182794Z Register a FakeTensor implementation ("fake impl") for this operator.
2025-03-04T20:58:55.8183149Z 
2025-03-04T20:58:55.8183355Z     Also sometimes known as a "meta kernel", "abstract impl".
2025-03-04T20:58:55.8183659Z 
2025-03-04T20:58:55.8183929Z     An "FakeTensor implementation" specifies the behavior of this operator on
2025-03-04T20:58:55.8184546Z     Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2025-03-04T20:58:55.8185152Z     certain properties (sizes/strides/storage_offset/device), it specifies
2025-03-04T20:58:55.8185677Z     what the properties of the output Tensors are.
2025-03-04T20:58:55.8185943Z 
2025-03-04T20:58:55.8186200Z     The FakeTensor implementation has the same signature as the operator.
2025-03-04T20:58:55.8186778Z     It is run for both FakeTensors and meta tensors. To write a FakeTensor
2025-03-04T20:58:55.8187355Z     implementation, assume that all Tensor inputs to the operator are
2025-03-04T20:58:55.8187919Z     regular CPU/CUDA/Meta tensors, but they do not have storage, and
2025-03-04T20:58:55.8188470Z     you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2025-03-04T20:58:55.8189043Z     The FakeTensor implementation must consist of only PyTorch operations
2025-03-04T20:58:55.8189603Z     (and may not directly access the storage or data of any input or
2025-03-04T20:58:55.8190036Z     intermediate Tensors).
2025-03-04T20:58:55.8190224Z 
2025-03-04T20:58:55.8190468Z     This API may be used as a decorator (see examples).
2025-03-04T20:58:55.8190892Z 
2025-03-04T20:58:55.8191128Z     For a detailed guide on custom ops, please see
2025-03-04T20:58:55.8191763Z     https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2025-03-04T20:58:55.8192137Z 
2025-03-04T20:58:55.8192287Z     Examples:
2025-03-04T20:58:55.8192543Z         >>> import torch
2025-03-04T20:58:55.8192842Z         >>> import numpy as np
2025-03-04T20:58:55.8193213Z         >>> from torch import Tensor
2025-03-04T20:58:55.8193528Z         >>>
2025-03-04T20:58:55.8193867Z         >>> # Example 1: an operator without data-dependent output shape
2025-03-04T20:58:55.8194434Z         >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2025-03-04T20:58:55.8195015Z         >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-03-04T20:58:55.8195560Z         >>>     raise NotImplementedError("Implementation goes here")
2025-03-04T20:58:55.8195962Z         >>>
2025-03-04T20:58:55.8196278Z         >>> @torch.library.register_fake("mylib::custom_linear")
2025-03-04T20:58:55.8196750Z         >>> def _(x, weight, bias):
2025-03-04T20:58:55.8197079Z         >>>     assert x.dim() == 2
2025-03-04T20:58:55.8197413Z         >>>     assert weight.dim() == 2
2025-03-04T20:58:55.8197758Z         >>>     assert bias.dim() == 1
2025-03-04T20:58:55.8198122Z         >>>     assert x.shape[1] == weight.shape[1]
2025-03-04T20:58:55.8198513Z         >>>     assert weight.shape[0] == bias.shape[0]
2025-03-04T20:58:55.8198900Z         >>>     assert x.device == weight.device
2025-03-04T20:58:55.8199242Z         >>>
2025-03-04T20:58:55.8199504Z         >>>     return (x @ weight.t()) + bias
2025-03-04T20:58:55.8199837Z         >>>
2025-03-04T20:58:55.8200159Z         >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2025-03-04T20:58:55.8200578Z         >>>     x = torch.randn(2, 3)
2025-03-04T20:58:55.8200911Z         >>>     w = torch.randn(3, 3)
2025-03-04T20:58:55.8201239Z         >>>     b = torch.randn(3)
2025-03-04T20:58:55.8201599Z         >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2025-03-04T20:58:55.8201956Z         >>>
2025-03-04T20:58:55.8202250Z         >>> assert y.shape == (2, 3)
2025-03-04T20:58:55.8202550Z         >>>
2025-03-04T20:58:55.8202873Z         >>> # Example 2: an operator with data-dependent output shape
2025-03-04T20:58:55.8203481Z         >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2025-03-04T20:58:55.8203972Z         >>> def custom_nonzero(x: Tensor) -> Tensor:
2025-03-04T20:58:55.8204349Z         >>>     x_np = x.numpy(force=True)
2025-03-04T20:58:55.8204725Z         >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-03-04T20:58:55.8205128Z         >>>     return torch.tensor(res, device=x.device)
2025-03-04T20:58:55.8205481Z         >>>
2025-03-04T20:58:55.8205799Z         >>> @torch.library.register_fake("mylib::custom_nonzero")
2025-03-04T20:58:55.8206194Z         >>> def _(x):
2025-03-04T20:58:55.8206518Z         >>> # Number of nonzero-elements is data-dependent.
2025-03-04T20:58:55.8206966Z         >>> # Since we cannot peek at the data in an fake impl,
2025-03-04T20:58:55.8207412Z         >>> # we use the ctx object to construct a new symint that
2025-03-04T20:58:55.8207838Z         >>> # represents the data-dependent size.
2025-03-04T20:58:55.8208222Z         >>>     ctx = torch.library.get_ctx()
2025-03-04T20:58:55.8208593Z         >>>     nnz = ctx.new_dynamic_size()
2025-03-04T20:58:55.8208942Z         >>>     shape = [nnz, x.dim()]
2025-03-04T20:58:55.8209323Z         >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-03-04T20:58:55.8209712Z         >>>     return result
2025-03-04T20:58:55.8210001Z         >>>
2025-03-04T20:58:55.8210317Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-04T20:58:55.8210721Z         >>>
2025-03-04T20:58:55.8210982Z         >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2025-03-04T20:58:55.8211484Z         >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2025-03-04T20:58:55.8211989Z         >>> trace.print_readable()
2025-03-04T20:58:55.8212300Z         >>>
2025-03-04T20:58:55.8212671Z         >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2025-03-04T20:58:55.8213037Z 
2025-03-04T20:58:55.8213128Z     
2025-03-04T20:58:55.8213798Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2025-03-04T20:58:55.8214455Z 
2025-03-04T20:58:55.8214550Z _._ = None
2025-03-04T20:58:55.8214778Z ^
2025-03-04T20:58:55.8215414Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1041.
2025-03-04T20:58:55.8216319Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8216886Z Register a backward formula for this custom op.
2025-03-04T20:58:55.8217152Z 
2025-03-04T20:58:55.8217382Z     In order for an operator to work with autograd, you need to register
2025-03-04T20:58:55.8217979Z     a backward formula:
2025-03-04T20:58:55.8218386Z     1. You must tell us how to compute gradients during the backward pass
2025-03-04T20:58:55.8218865Z     by providing us a "backward" function.
2025-03-04T20:58:55.8219333Z     2. If you need any values from the forward to compute gradients, you can
2025-03-04T20:58:55.8219841Z     use `setup_context` to save values for backward.
2025-03-04T20:58:55.8220104Z 
2025-03-04T20:58:55.8220361Z     ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2025-03-04T20:58:55.8220933Z     - ``grads`` is one or more gradients. The number of gradients matches
2025-03-04T20:58:55.8221404Z     the number of outputs of the operator.
2025-03-04T20:58:55.8221881Z     The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2025-03-04T20:58:55.8222486Z     :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2025-03-04T20:58:55.8223028Z     same as :meth:`torch.autograd.Function.backward`.
2025-03-04T20:58:55.8223310Z 
2025-03-04T20:58:55.8223542Z     ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2025-03-04T20:58:55.8224172Z     Please save quantities needed for backward onto the ``ctx`` object via
2025-03-04T20:58:55.8224774Z     either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2025-03-04T20:58:55.8225408Z     or assigning them as attributes of ``ctx``. If your custom op has
2025-03-04T20:58:55.8225962Z     kwarg-only arguments, we expect the signature of ``setup_context``
2025-03-04T20:58:55.8226515Z     to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2025-03-04T20:58:55.8226844Z 
2025-03-04T20:58:55.8227070Z     Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2025-03-04T20:58:55.8227661Z     they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2025-03-04T20:58:55.8228260Z     not depend on or mutate global state. If you need a non-traceable backward,
2025-03-04T20:58:55.8228861Z     you can make it a separate custom_op that you call inside ``backward_fn``.
2025-03-04T20:58:55.8229228Z 
2025-03-04T20:58:55.8229457Z     If you need different autograd behavior on different devices, then we
2025-03-04T20:58:55.8230046Z     recommend creating two different custom operators, one for each device
2025-03-04T20:58:55.8230642Z     that needs different behavior, and switching between them at runtime.
2025-03-04T20:58:55.8231001Z 
2025-03-04T20:58:55.8231099Z     Examples:
2025-03-04T20:58:55.8231349Z         >>> import torch
2025-03-04T20:58:55.8231640Z         >>> import numpy as np
2025-03-04T20:58:55.8231955Z         >>> from torch import Tensor
2025-03-04T20:58:55.8232276Z         >>>
2025-03-04T20:58:55.8232637Z         >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2025-03-04T20:58:55.8233103Z         >>> def numpy_sin(x: Tensor) -> Tensor:
2025-03-04T20:58:55.8233466Z         >>>     x_np = x.cpu().numpy()
2025-03-04T20:58:55.8233799Z         >>>     y_np = np.sin(x_np)
2025-03-04T20:58:55.8234182Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-04T20:58:55.8234564Z         >>>
2025-03-04T20:58:55.8234860Z         >>> def setup_context(ctx, inputs, output) -> Tensor:
2025-03-04T20:58:55.8235230Z         >>>     x, = inputs
2025-03-04T20:58:55.8235535Z         >>>     ctx.save_for_backward(x)
2025-03-04T20:58:55.8235903Z         >>>
2025-03-04T20:58:55.8236282Z         >>> def backward(ctx, grad):
2025-03-04T20:58:55.8236744Z         >>>     x, = ctx.saved_tensors
2025-03-04T20:58:55.8237279Z         >>>     return grad * x.cos()
2025-03-04T20:58:55.8237769Z         >>>
2025-03-04T20:58:55.8238185Z         >>> torch.library.register_autograd(
2025-03-04T20:58:55.8238640Z         ...     "mylib::numpy_sin", backward, setup_context=setup_context
2025-03-04T20:58:55.8239051Z         ... )
2025-03-04T20:58:55.8239285Z         >>>
2025-03-04T20:58:55.8239557Z         >>> x = torch.randn(3, requires_grad=True)
2025-03-04T20:58:55.8240038Z         >>> y = numpy_sin(x)
2025-03-04T20:58:55.8240415Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-04T20:58:55.8240859Z         >>> assert torch.allclose(grad_x, x.cos())
2025-03-04T20:58:55.8241204Z         >>>
2025-03-04T20:58:55.8241474Z         >>> # Example with a keyword-only arg
2025-03-04T20:58:55.8241922Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-04T20:58:55.8242422Z         >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2025-03-04T20:58:55.8242819Z         >>>     x_np = x.cpu().numpy()
2025-03-04T20:58:55.8243149Z         >>>     y_np = x_np * val
2025-03-04T20:58:55.8243521Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-04T20:58:55.8243898Z         >>>
2025-03-04T20:58:55.8244268Z         >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2025-03-04T20:58:55.8244770Z         >>>     ctx.val = keyword_only_inputs["val"]
2025-03-04T20:58:55.8245114Z         >>>
2025-03-04T20:58:55.8245364Z         >>> def backward(ctx, grad):
2025-03-04T20:58:55.8245693Z         >>>     return grad * ctx.val
2025-03-04T20:58:55.8246046Z         >>>
2025-03-04T20:58:55.8246317Z         >>> torch.library.register_autograd(
2025-03-04T20:58:55.8246749Z         ...     "mylib::numpy_mul", backward, setup_context=setup_context
2025-03-04T20:58:55.8247156Z         ... )
2025-03-04T20:58:55.8247447Z         >>>
2025-03-04T20:58:55.8247718Z         >>> x = torch.randn(3, requires_grad=True)
2025-03-04T20:58:55.8248084Z         >>> y = numpy_mul(x, val=3.14)
2025-03-04T20:58:55.8248473Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-04T20:58:55.8248963Z         >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2025-03-04T20:58:55.8249279Z 
2025-03-04T20:58:55.8249371Z     
2025-03-04T20:58:55.8249770Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8250163Z 
2025-03-04T20:58:55.8250685Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1455.
2025-03-04T20:58:55.8251552Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8252175Z Given an operator and some sample arguments, tests if the operator is
2025-03-04T20:58:55.8252633Z     registered correctly.
2025-03-04T20:58:55.8252822Z 
2025-03-04T20:58:55.8253057Z     That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2025-03-04T20:58:55.8253654Z     custom op, you specified metadata (e.g. mutability info) about the custom op
2025-03-04T20:58:55.8254343Z     and these APIs require that the functions you pass them satisfy certain
2025-03-04T20:58:55.8254941Z     properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2025-03-04T20:58:55.8255449Z     ``opcheck`` tests these metadata and properties.
2025-03-04T20:58:55.8255729Z 
2025-03-04T20:58:55.8255855Z     Concretely, we test the following:
2025-03-04T20:58:55.8256104Z 
2025-03-04T20:58:55.8256296Z     - test_schema: If the schema matches the implementation of
2025-03-04T20:58:55.8256845Z       the operator. For example: if the schema specifies a Tensor is mutated,
2025-03-04T20:58:55.8257423Z       then we check the implementation mutates the Tensor. If the schema
2025-03-04T20:58:55.8258039Z       specifies that we return a new Tensor, then we check that the
2025-03-04T20:58:55.8258597Z       implementation returns a new Tensor (instead of an existing one or
2025-03-04T20:58:55.8259072Z       a view of an existing one).
2025-03-04T20:58:55.8259506Z     - test_autograd_registration: If the operator supports training
2025-03-04T20:58:55.8260050Z       (autograd): we check that its autograd formula is registered via
2025-03-04T20:58:55.8260600Z       torch.library.register_autograd or a manual registration to one
2025-03-04T20:58:55.8261172Z       or more DispatchKey::Autograd keys. Any other DispatchKey-based
2025-03-04T20:58:55.8261655Z       registrations may lead to undefined behavior.
2025-03-04T20:58:55.8262166Z     - test_faketensor: If the operator has a FakeTensor kernel
2025-03-04T20:58:55.8262666Z       (and if it is correct). The FakeTensor kernel is necessary (
2025-03-04T20:58:55.8263207Z       but not sufficient) for the operator to work with PyTorch compilation
2025-03-04T20:58:55.8263790Z       APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2025-03-04T20:58:55.8264342Z       (also sometimes known as a meta kernel) was registered for the
2025-03-04T20:58:55.8264879Z       operator and that it is correct. This test takes the result of
2025-03-04T20:58:55.8265414Z       running the operator on real tensors and the result of running
2025-03-04T20:58:55.8265941Z       the operator on FakeTensors and checks that they have the same
2025-03-04T20:58:55.8266427Z       Tensor metadata (sizes/strides/dtype/device/etc).
2025-03-04T20:58:55.8266917Z     - test_aot_dispatch_dynamic: If the operator has correct behavior
2025-03-04T20:58:55.8267439Z       with PyTorch compilation APIs (torch.compile/export/FX).
2025-03-04T20:58:55.8267970Z       This checks that the outputs (and gradients, if applicable) are the
2025-03-04T20:58:55.8268507Z       same under eager-mode PyTorch and torch.compile.
2025-03-04T20:58:55.8268984Z       This test is a superset of ``test_faketensor`` and is an e2e test;
2025-03-04T20:58:55.8269533Z       other things it tests are that the operator supports
2025-03-04T20:58:55.8270054Z       functionalization and that the backward pass (if it exists) also
2025-03-04T20:58:55.8270547Z       supports FakeTensor and functionalization.
2025-03-04T20:58:55.8270816Z 
2025-03-04T20:58:55.8271023Z     For best results, please call ``opcheck`` multiple times with a
2025-03-04T20:58:55.8271543Z     representative set of inputs. If your operator supports
2025-03-04T20:58:55.8272102Z     autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2025-03-04T20:58:55.8272706Z     if your operator supports multiple devices (e.g. CPU and CUDA), please
2025-03-04T20:58:55.8273238Z     use ``opcheck`` with inputs on all supported devices.
2025-03-04T20:58:55.8273531Z 
2025-03-04T20:58:55.8273827Z     Args:
2025-03-04T20:58:55.8274162Z         op: The operator. Must either be a function decorated with
2025-03-04T20:58:55.8274701Z             :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2025-03-04T20:58:55.8275289Z             found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2025-03-04T20:58:55.8275760Z         args: The args to the operator
2025-03-04T20:58:55.8276112Z         kwargs: The kwargs to the operator
2025-03-04T20:58:55.8276542Z         test_utils: Tests that we should run. Default: all of them.
2025-03-04T20:58:55.8276996Z             Example: ("test_schema", "test_faketensor")
2025-03-04T20:58:55.8277466Z         raise_exception: If we should raise an exception on the first
2025-03-04T20:58:55.8277969Z             error. If False, we will return a dict with information
2025-03-04T20:58:55.8278394Z             on if each test passed or not.
2025-03-04T20:58:55.8278885Z         rtol (Optional[float]): Relative tolerance for floating point comparisons.
2025-03-04T20:58:55.8279415Z             If specified ``atol`` must also be specified.
2025-03-04T20:58:55.8279883Z             If omitted, default values based on the ``dtype`` are selected
2025-03-04T20:58:55.8280388Z             (see the table in :func:`torch.testing.assert_close`).
2025-03-04T20:58:55.8280931Z         atol (Optional[float]): Absolute tolerance for floating point comparisons.
2025-03-04T20:58:55.8281456Z             If specified ``rtol`` must also be specified.
2025-03-04T20:58:55.8281917Z             If omitted, default values based on the ``dtype`` are selected
2025-03-04T20:58:55.8282404Z             (see the table in :func:`torch.testing.assert_close`).
2025-03-04T20:58:55.8282703Z 
2025-03-04T20:58:55.8282812Z     .. warning::
2025-03-04T20:58:55.8282977Z 
2025-03-04T20:58:55.8283205Z         opcheck and :func:`torch.autograd.gradcheck` test different things;
2025-03-04T20:58:55.8283871Z         opcheck tests if your usage of torch.library APIs is correct while
2025-03-04T20:58:55.8284443Z         :func:`torch.autograd.gradcheck` tests if your autograd formula is
2025-03-04T20:58:55.8285018Z         mathematically correct. Use both to test custom ops that support
2025-03-04T20:58:55.8285488Z         gradient computation.
2025-03-04T20:58:55.8285701Z 
2025-03-04T20:58:55.8285797Z     Example:
2025-03-04T20:58:55.8285948Z 
2025-03-04T20:58:55.8286096Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:58:55.8288474Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-04T20:58:55.8288977Z         >>> def numpy_mul(x: Tensor, y: float) -> Tensor:
2025-03-04T20:58:55.8289369Z         >>>     x_np = x.numpy(force=True)
2025-03-04T20:58:55.8289710Z         >>>     z_np = x_np * y
2025-03-04T20:58:55.8290053Z         >>>     return torch.from_numpy(z_np).to(x.device)
2025-03-04T20:58:55.8290418Z         >>>
2025-03-04T20:58:55.8290668Z         >>> @numpy_mul.register_fake
2025-03-04T20:58:55.8290993Z         >>> def _(x, y):
2025-03-04T20:58:55.8291375Z         >>>     return torch.empty_like(x)
2025-03-04T20:58:55.8291703Z         >>>
2025-03-04T20:58:55.8291978Z         >>> def setup_context(ctx, inputs, output):
2025-03-04T20:58:55.8292441Z         >>>     y, = inputs
2025-03-04T20:58:55.8292733Z         >>>     ctx.y = y
2025-03-04T20:58:55.8292994Z         >>>
2025-03-04T20:58:55.8293248Z         >>> def backward(ctx, grad):
2025-03-04T20:58:55.8293589Z         >>>     return grad * ctx.y, None
2025-03-04T20:58:55.8293916Z         >>>
2025-03-04T20:58:55.8294283Z         >>> numpy_mul.register_autograd(backward, setup_context=setup_context)
2025-03-04T20:58:55.8294731Z         >>>
2025-03-04T20:58:55.8294975Z         >>> sample_inputs = [
2025-03-04T20:58:55.8295285Z         >>>     (torch.randn(3), 3.14),
2025-03-04T20:58:55.8295646Z         >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2025-03-04T20:58:55.8296068Z         >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2025-03-04T20:58:55.8296550Z         >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2025-03-04T20:58:55.8296975Z         >>> ]
2025-03-04T20:58:55.8297214Z         >>>
2025-03-04T20:58:55.8297470Z         >>> for args in sample_inputs:
2025-03-04T20:58:55.8297924Z         >>>     torch.library.opcheck(numpy_mul, args)
2025-03-04T20:58:55.8298186Z 
2025-03-04T20:58:55.8298291Z     
2025-03-04T20:58:55.8298685Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8299063Z 
2025-03-04T20:58:55.8681709Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1247.
2025-03-04T20:58:55.8683046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:55.8683786Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args)
2025-03-04T20:58:55.8684263Z 
2025-03-04T20:58:55.8684469Z     Loads an object saved with :func:`torch.save` from a file.
2025-03-04T20:58:55.8684776Z 
2025-03-04T20:58:55.8685037Z     :func:`torch.load` uses Python's unpickling facilities but treats storages,
2025-03-04T20:58:55.8685656Z     which underlie tensors, specially. They are first deserialized on the
2025-03-04T20:58:55.8686247Z     CPU and are then moved to the device they were saved from. If this fails
2025-03-04T20:58:55.8686843Z     (e.g. because the run time system doesn't have certain devices), an exception
2025-03-04T20:58:55.8687459Z     is raised. However, storages can be dynamically remapped to an alternative
2025-03-04T20:58:55.8688039Z     set of devices using the :attr:`map_location` argument.
2025-03-04T20:58:55.8688327Z 
2025-03-04T20:58:55.8688592Z     If :attr:`map_location` is a callable, it will be called once for each serialized
2025-03-04T20:58:55.8689206Z     storage with two arguments: storage and location. The storage argument
2025-03-04T20:58:55.8689975Z     will be the initial deserialization of the storage, residing on the CPU.
2025-03-04T20:58:55.8690568Z     Each serialized storage has a location tag associated with it which
2025-03-04T20:58:55.8691139Z     identifies the device it was saved from, and this tag is the second
2025-03-04T20:58:55.8691748Z     argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2025-03-04T20:58:55.8692364Z     for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2025-03-04T20:58:55.8692935Z     :attr:`map_location` should return either ``None`` or a storage. If
2025-03-04T20:58:55.8693534Z     :attr:`map_location` returns a storage, it will be used as the final deserialized
2025-03-04T20:58:55.8694171Z     object, already moved to the right device. Otherwise, :func:`torch.load` will
2025-03-04T20:58:55.8694789Z     fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2025-03-04T20:58:55.8695172Z 
2025-03-04T20:58:55.8695428Z     If :attr:`map_location` is a :class:`torch.device` object or a string containing
2025-03-04T20:58:55.8696101Z     a device tag, it indicates the location where all tensors should be loaded.
2025-03-04T20:58:55.8696475Z 
2025-03-04T20:58:55.8696742Z     Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2025-03-04T20:58:55.8697484Z     appearing in the file (keys), to ones that specify where to put the
2025-03-04T20:58:55.8698022Z     storages (values).
2025-03-04T20:58:55.8698207Z 
2025-03-04T20:58:55.8698437Z     User extensions can register their own location tags and tagging and
2025-03-04T20:58:55.8699066Z     deserialization methods using :func:`torch.serialization.register_package`.
2025-03-04T20:58:55.8699465Z 
2025-03-04T20:58:55.8699562Z     Args:
2025-03-04T20:58:55.8700027Z         f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2025-03-04T20:58:55.8700665Z             or a string or os.PathLike object containing a file name
2025-03-04T20:58:55.8701301Z         map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2025-03-04T20:58:55.8701859Z             locations
2025-03-04T20:58:55.8702269Z         pickle_module: module used for unpickling metadata and objects (has to
2025-03-04T20:58:55.8702819Z             match the :attr:`pickle_module` used to serialize file)
2025-03-04T20:58:55.8703345Z         weights_only: Indicates whether unpickler should be restricted to
2025-03-04T20:58:55.8703869Z             loading only tensors, primitive types, dictionaries
2025-03-04T20:58:55.8704393Z             and any types added via :func:`torch.serialization.add_safe_globals`.
2025-03-04T20:58:55.8704888Z             See :ref:`weights-only` for more details.
2025-03-04T20:58:55.8705492Z         mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
2025-03-04T20:58:55.8706306Z             Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2025-03-04T20:58:55.8707124Z             are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2025-03-04T20:58:55.8707945Z             second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2025-03-04T20:58:55.8708655Z             tensor storages from disk to CPU memory in the first step, ``f`` is mmaped.
2025-03-04T20:58:55.8709282Z         pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2025-03-04T20:58:55.8709893Z             :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2025-03-04T20:58:55.8710357Z             :attr:`errors=...`.
2025-03-04T20:58:55.8710550Z 
2025-03-04T20:58:55.8710686Z     .. warning::
2025-03-04T20:58:55.8711065Z         :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2025-03-04T20:58:55.8711618Z         uses ``pickle`` module implicitly, which is known to be insecure.
2025-03-04T20:58:55.8712284Z         It is possible to construct malicious pickle data which will execute arbitrary code
2025-03-04T20:58:55.8712960Z         during unpickling. Never load data that could have come from an untrusted
2025-03-04T20:58:55.8713648Z         source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2025-03-04T20:58:55.8714069Z 
2025-03-04T20:58:55.8714180Z     .. note::
2025-03-04T20:58:55.8714595Z         When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2025-03-04T20:58:55.8715258Z         will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2025-03-04T20:58:55.8715929Z         and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2025-03-04T20:58:55.8716327Z 
2025-03-04T20:58:55.8716433Z     .. note::
2025-03-04T20:58:55.8716835Z         By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2025-03-04T20:58:55.8717443Z         case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2025-03-04T20:58:55.8718034Z         when loading files saved by Python 2 in Python 3.  If this default
2025-03-04T20:58:55.8718638Z         is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2025-03-04T20:58:55.8719342Z         these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2025-03-04T20:58:55.8719970Z         to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2025-03-04T20:58:55.8720580Z         as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2025-03-04T20:58:55.8720946Z 
2025-03-04T20:58:55.8721046Z     Example:
2025-03-04T20:58:55.8721334Z         >>> # xdoctest: +SKIP("undefined filepaths")
2025-03-04T20:58:55.8721746Z         >>> torch.load("tensors.pt", weights_only=True)
2025-03-04T20:58:55.8722138Z         # Load all tensors onto the CPU
2025-03-04T20:58:55.8722478Z         >>> torch.load(
2025-03-04T20:58:55.8722763Z         ...     "tensors.pt",
2025-03-04T20:58:55.8723099Z         ...     map_location=torch.device("cpu"),
2025-03-04T20:58:55.8723465Z         ...     weights_only=True,
2025-03-04T20:58:55.8723773Z         ... )
2025-03-04T20:58:55.8724068Z         # Load all tensors onto the CPU, using a function
2025-03-04T20:58:55.8724442Z         >>> torch.load(
2025-03-04T20:58:55.8724721Z         ...     "tensors.pt",
2025-03-04T20:58:55.8725063Z         ...     map_location=lambda storage, loc: storage,
2025-03-04T20:58:55.8725445Z         ...     weights_only=True,
2025-03-04T20:58:55.8725753Z         ... )
2025-03-04T20:58:55.8726001Z         # Load all tensors onto GPU 1
2025-03-04T20:58:55.8726333Z         >>> torch.load(
2025-03-04T20:58:55.8726612Z         ...     "tensors.pt",
2025-03-04T20:58:55.8726984Z         ...     map_location=lambda storage, loc: storage.cuda(1),
2025-03-04T20:58:55.8727388Z         ...     weights_only=True,
2025-03-04T20:58:55.8727729Z         ... )  # type: ignore[attr-defined]
2025-03-04T20:58:55.8728092Z         # Map tensors from GPU 1 to GPU 0
2025-03-04T20:58:55.8728435Z         >>> torch.load(
2025-03-04T20:58:55.8728717Z         ...     "tensors.pt",
2025-03-04T20:58:55.8729045Z         ...     map_location={"cuda:1": "cuda:0"},
2025-03-04T20:58:55.8729410Z         ...     weights_only=True,
2025-03-04T20:58:55.8729721Z         ... )
2025-03-04T20:58:55.8729994Z         # Load tensor from io.BytesIO object
2025-03-04T20:58:55.8730502Z         # Loading from a buffer setting weights_only=False, warning this can be unsafe
2025-03-04T20:58:55.8731021Z         >>> with open("tensor.pt", "rb") as f:
2025-03-04T20:58:55.8747908Z         ...     buffer = io.BytesIO(f.read())
2025-03-04T20:58:55.8748401Z         >>> torch.load(buffer, weights_only=False)
2025-03-04T20:58:55.8748835Z         # Load a module with 'ascii' encoding for unpickling
2025-03-04T20:58:55.8749380Z         # Loading from a module setting weights_only=False, warning this can be unsafe
2025-03-04T20:58:55.8750089Z         >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2025-03-04T20:58:55.8750513Z     
2025-03-04T20:58:55.8750914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:55.8751308Z 
2025-03-04T20:58:55.9826680Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=37.
2025-03-04T20:58:55.9827613Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:58:55.9828190Z Check if there is an available :ref:`accelerator<accelerators>`.
2025-03-04T20:58:55.9828528Z 
2025-03-04T20:58:55.9828627Z     Returns:
2025-03-04T20:58:55.9829063Z         bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2025-03-04T20:58:55.9829482Z 
2025-03-04T20:58:55.9829602Z     Example::
2025-03-04T20:58:55.9829755Z 
2025-03-04T20:58:55.9830044Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:58:55.9830536Z     
2025-03-04T20:58:55.9831409Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2025-03-04T20:58:55.9832106Z 
2025-03-04T20:58:55.9832496Z assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:58:55.9833003Z                                         ^
2025-03-04T20:58:55.9844904Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=138.
2025-03-04T20:58:55.9845865Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:58:55.9846466Z Wait for all kernels in all streams on the given device to complete.
2025-03-04T20:58:55.9846812Z 
2025-03-04T20:58:55.9846906Z     Args:
2025-03-04T20:58:55.9847375Z         device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2025-03-04T20:58:55.9848087Z             the current :ref:`accelerator<accelerators>` device type. If not given,
2025-03-04T20:58:55.9848672Z             use :func:`torch.accelerator.current_device_index` by default.
2025-03-04T20:58:55.9849008Z 
2025-03-04T20:58:55.9849348Z     .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2025-03-04T20:58:55.9849803Z 
2025-03-04T20:58:55.9849907Z     Example::
2025-03-04T20:58:55.9850065Z 
2025-03-04T20:58:55.9850218Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:58:55.9850772Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:58:55.9851331Z         >>> start_event = torch.Event(enable_timing=True)
2025-03-04T20:58:55.9851753Z         >>> end_event = torch.Event(enable_timing=True)
2025-03-04T20:58:55.9852134Z         >>> start_event.record()
2025-03-04T20:58:55.9852599Z         >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2025-03-04T20:58:55.9853092Z         >>> sum = torch.sum(tensor)
2025-03-04T20:58:55.9853422Z         >>> end_event.record()
2025-03-04T20:58:55.9853756Z         >>> torch.accelerator.synchronize()
2025-03-04T20:58:55.9854188Z         >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2025-03-04T20:58:55.9854582Z     
2025-03-04T20:58:55.9855284Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2025-03-04T20:58:55.9855971Z 
2025-03-04T20:58:55.9856267Z assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:58:55.9856770Z                                         ^
2025-03-04T20:58:56.0099015Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=400.
2025-03-04T20:58:56.0099905Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:58:56.0100647Z Retrieves the CUDA runtime API module.
2025-03-04T20:58:56.0100883Z 
2025-03-04T20:58:56.0100888Z 
2025-03-04T20:58:56.0101166Z     This function initializes the CUDA runtime environment if it is not already
2025-03-04T20:58:56.0101824Z     initialized and returns the CUDA runtime API module (_cudart). The CUDA
2025-03-04T20:58:56.0102426Z     runtime API module provides access to various CUDA runtime functions.
2025-03-04T20:58:56.0102779Z 
2025-03-04T20:58:56.0102890Z     Args:
2025-03-04T20:58:56.0103126Z         ``None``
2025-03-04T20:58:56.0103272Z 
2025-03-04T20:58:56.0103381Z     Returns:
2025-03-04T20:58:56.0103680Z         module: The CUDA runtime API module (_cudart).
2025-03-04T20:58:56.0103944Z 
2025-03-04T20:58:56.0104052Z     Raises:
2025-03-04T20:58:56.0104437Z         RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2025-03-04T20:58:56.0105172Z         AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2025-03-04T20:58:56.0105660Z 
2025-03-04T20:58:56.0105813Z     Example of CUDA operations with profiling:
2025-03-04T20:58:56.0106250Z         >>> import torch
2025-03-04T20:58:56.0106581Z         >>> from torch.cuda import cudart, check_error
2025-03-04T20:58:56.0106935Z         >>> import os
2025-03-04T20:58:56.0107314Z         >>>
2025-03-04T20:58:56.0107596Z         >>> os.environ['CUDA_PROFILE'] = '1'
2025-03-04T20:58:56.0107937Z         >>>
2025-03-04T20:58:56.0108223Z         >>> def perform_cuda_operations_with_streams():
2025-03-04T20:58:56.0108624Z         >>>     stream = torch.cuda.Stream()
2025-03-04T20:58:56.0108999Z         >>>     with torch.cuda.stream(stream):
2025-03-04T20:58:56.0109392Z         >>>         x = torch.randn(100, 100, device='cuda')
2025-03-04T20:58:56.0109787Z         >>>         y = torch.randn(100, 100, device='cuda')
2025-03-04T20:58:56.0110159Z         >>>         z = torch.mul(x, y)
2025-03-04T20:58:56.0110489Z         >>>     return z
2025-03-04T20:58:56.0110762Z         >>>
2025-03-04T20:58:56.0111092Z         >>> torch.cuda.synchronize()
2025-03-04T20:58:56.0111465Z         >>> print("====== Start nsys profiling ======")
2025-03-04T20:58:56.0111873Z         >>> check_error(cudart().cudaProfilerStart())
2025-03-04T20:58:56.0112285Z         >>> with torch.autograd.profiler.emit_nvtx():
2025-03-04T20:58:56.0112727Z         >>>     result = perform_cuda_operations_with_streams()
2025-03-04T20:58:56.0113153Z         >>>     print("CUDA operations completed.")
2025-03-04T20:58:56.0113576Z         >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2025-03-04T20:58:56.0114006Z         >>> print("====== End nsys profiling ======")
2025-03-04T20:58:56.0114251Z 
2025-03-04T20:58:56.0114474Z     To run this example and save the profiling information, execute:
2025-03-04T20:58:56.0115182Z         >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-04T20:58:56.0115666Z 
2025-03-04T20:58:56.0115933Z     This command profiles the CUDA operations in the provided script and saves
2025-03-04T20:58:56.0116517Z     the profiling information to a file named `trace_name.prof`.
2025-03-04T20:58:56.0117105Z     The `--profile-from-start off` option ensures that profiling starts only
2025-03-04T20:58:56.0117635Z     after the `cudaProfilerStart` call in the script.
2025-03-04T20:58:56.0118147Z     The `--csv` and `--print-summary` options format the profiling output as a
2025-03-04T20:58:56.0118647Z     CSV file and print a summary, respectively.
2025-03-04T20:58:56.0119163Z     The `-o` option specifies the output file name, and the `-f` option forces the
2025-03-04T20:58:56.0119705Z     overwrite of the output file if it already exists.
2025-03-04T20:58:56.0120078Z     
2025-03-04T20:58:56.0120863Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2025-03-04T20:58:56.0121692Z 
2025-03-04T20:58:56.0122059Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-04T20:58:56.0122638Z ^
2025-03-04T20:58:56.0255881Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=105.
2025-03-04T20:58:56.0256822Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:56.0257215Z 
2025-03-04T20:58:56.0257468Z Append the given callback function to this ``Future``, which will be run
2025-03-04T20:58:56.0258103Z when the ``Future`` is completed.  Multiple callbacks can be added to
2025-03-04T20:58:56.0258657Z the same ``Future``, but the order in which they will be executed cannot
2025-03-04T20:58:56.0259196Z be guaranteed (to enforce a certain order consider chaining:
2025-03-04T20:58:56.0259727Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2025-03-04T20:58:56.0260283Z is the reference to this ``Future``. The callback function can use the
2025-03-04T20:58:56.0260825Z :meth:`value` method to get the value. Note that if this ``Future`` is
2025-03-04T20:58:56.0261570Z already completed, the given callback will be run immediately inline.
2025-03-04T20:58:56.0261936Z 
2025-03-04T20:58:56.0262243Z If the ``Future``'s value contains tensors that reside on GPUs, the
2025-03-04T20:58:56.0262798Z callback might be invoked while the async kernels that are populating
2025-03-04T20:58:56.0263388Z those tensors haven't yet finished executing on the device. However, the
2025-03-04T20:58:56.0263967Z callback will be invoked with some dedicated streams set as current
2025-03-04T20:58:56.0264522Z (fetched from a global pool) which will be synchronized with those
2025-03-04T20:58:56.0265087Z kernels. Hence any operation performed by the callback on these tensors
2025-03-04T20:58:56.0265869Z will be scheduled on the device after the kernels complete. In other
2025-03-04T20:58:56.0266873Z words, as long as the callback doesn't switch streams, it can safely
2025-03-04T20:58:56.0267495Z manipulate the result without any additional synchronization. This is
2025-03-04T20:58:56.0268026Z similar to the non-blocking behavior of :meth:`wait`.
2025-03-04T20:58:56.0268315Z 
2025-03-04T20:58:56.0268543Z Similarly, if the callback returns a value that contains tensors that
2025-03-04T20:58:56.0269093Z reside on a GPU, it can do so even if the kernels that are producing
2025-03-04T20:58:56.0269648Z these tensors are still running on the device, as long as the callback
2025-03-04T20:58:56.0270207Z didn't change streams during its execution. If one wants to change
2025-03-04T20:58:56.0270765Z streams, one must be careful to re-synchronize them with the original
2025-03-04T20:58:56.0271329Z streams, that is, those that were current when the callback was invoked.
2025-03-04T20:58:56.0271676Z 
2025-03-04T20:58:56.0271768Z Args:
2025-03-04T20:58:56.0272109Z     callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2025-03-04T20:58:56.0272565Z                             the only argument.
2025-03-04T20:58:56.0272807Z 
2025-03-04T20:58:56.0272901Z Returns:
2025-03-04T20:58:56.0273210Z     A new ``Future`` object that holds the return value of the
2025-03-04T20:58:56.0273891Z     ``callback`` and will be marked as completed when the given
2025-03-04T20:58:56.0274318Z     ``callback`` finishes.
2025-03-04T20:58:56.0274500Z 
2025-03-04T20:58:56.0274716Z .. note:: Note that if the callback function throws, either
2025-03-04T20:58:56.0275235Z     through the original future being completed with an exception and
2025-03-04T20:58:56.0275785Z     calling ``fut.wait()``, or through other code in the callback, the
2025-03-04T20:58:56.0276326Z     future returned by ``then`` will be marked appropriately with the
2025-03-04T20:58:56.0276878Z     encountered error. However, if this callback later completes
2025-03-04T20:58:56.0277436Z     additional futures, those futures are not marked as completed with
2025-03-04T20:58:56.0278105Z     an error and the user is responsible for handling completion/waiting
2025-03-04T20:58:56.0278570Z     on those futures independently.
2025-03-04T20:58:56.0278788Z 
2025-03-04T20:58:56.0278900Z Example::
2025-03-04T20:58:56.0279199Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-04T20:58:56.0279569Z     >>> def callback(fut):
2025-03-04T20:58:56.0279913Z     ...     print(f"RPC return value is {fut.wait()}.")
2025-03-04T20:58:56.0280295Z     >>> fut = torch.futures.Future()
2025-03-04T20:58:56.0280699Z     >>> # The inserted callback will print the return value when
2025-03-04T20:58:56.0281128Z     >>> # receiving the response from "worker1"
2025-03-04T20:58:56.0281491Z     >>> cb_fut = fut.then(callback)
2025-03-04T20:58:56.0281820Z     >>> chain_cb_fut = cb_fut.then(
2025-03-04T20:58:56.0282195Z     ...     lambda x : print(f"Chained cb done. {x.wait()}")
2025-03-04T20:58:56.0282564Z     ... )
2025-03-04T20:58:56.0282830Z     >>> fut.set_result(5)
2025-03-04T20:58:56.0283118Z     RPC return value is 5.
2025-03-04T20:58:56.0283419Z     Chained cb done. None
2025-03-04T20:58:56.0283671Z 
2025-03-04T20:58:56.0283937Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:56.0284327Z 
2025-03-04T20:58:56.0284970Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=213.
2025-03-04T20:58:56.0285908Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:56.0286312Z 
2025-03-04T20:58:56.0286526Z Set the result for this ``Future``, which will mark this ``Future`` as
2025-03-04T20:58:56.0287095Z completed and trigger all attached callbacks. Note that a ``Future``
2025-03-04T20:58:56.0287567Z cannot be marked completed twice.
2025-03-04T20:58:56.0287795Z 
2025-03-04T20:58:56.0288024Z If the result contains tensors that reside on GPUs, this method can be
2025-03-04T20:58:56.0288592Z called even if the asynchronous kernels that are populating those
2025-03-04T20:58:56.0289162Z tensors haven't yet completed running on the device, provided that the
2025-03-04T20:58:56.0289754Z streams on which those kernels were enqueued are set as the current ones
2025-03-04T20:58:56.0290336Z when this method is called. Put simply, it's safe to call this method
2025-03-04T20:58:56.0290913Z immediately after launching those kernels, without any additional
2025-03-04T20:58:56.0291496Z synchronization, as long as one doesn't change streams in between. This
2025-03-04T20:58:56.0292085Z method will record events on all the relevant current streams and will
2025-03-04T20:58:56.0292652Z use them to ensure proper scheduling for all the consumers of this
2025-03-04T20:58:56.0293081Z ``Future``.
2025-03-04T20:58:56.0293217Z 
2025-03-04T20:58:56.0293320Z Args:
2025-03-04T20:58:56.0293620Z     result (object): the result object of this ``Future``.
2025-03-04T20:58:56.0293901Z 
2025-03-04T20:58:56.0294008Z Example::
2025-03-04T20:58:56.0294298Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-04T20:58:56.0294670Z     >>> import threading
2025-03-04T20:58:56.0294950Z     >>> import time
2025-03-04T20:58:56.0295229Z     >>> def slow_set_future(fut, value):
2025-03-04T20:58:56.0295566Z     ...     time.sleep(0.5)
2025-03-04T20:58:56.0295854Z     ...     fut.set_result(value)
2025-03-04T20:58:56.0296187Z     >>> fut = torch.futures.Future()
2025-03-04T20:58:56.0296525Z     >>> t = threading.Thread(
2025-03-04T20:58:56.0296837Z     ...     target=slow_set_future,
2025-03-04T20:58:56.0297174Z     ...     args=(fut, torch.ones(2) * 3)
2025-03-04T20:58:56.0297500Z     ... )
2025-03-04T20:58:56.0297815Z     >>> t.start()
2025-03-04T20:58:56.0298083Z     >>> print(fut.wait())
2025-03-04T20:58:56.0298370Z     tensor([3., 3.])
2025-03-04T20:58:56.0298632Z     >>> t.join()
2025-03-04T20:58:56.0298777Z 
2025-03-04T20:58:56.0299055Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:56.0299481Z 
2025-03-04T20:58:56.0403065Z msg = Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144.
2025-03-04T20:58:56.0404015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:56.0404643Z Compiles compute shader from source and allows one to invoke kernels
2025-03-04T20:58:56.0405161Z     defined there from the comfort of Python runtime
2025-03-04T20:58:56.0405557Z     Example::
2025-03-04T20:58:56.0405712Z 
2025-03-04T20:58:56.0405873Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)
2025-03-04T20:58:56.0406267Z         >>> lib = torch.mps._compile_shader(
2025-03-04T20:58:56.0406908Z         ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"
2025-03-04T20:58:56.0407519Z         ...  )
2025-03-04T20:58:56.0407793Z         >>> x = torch.zeros(16, device="mps")
2025-03-04T20:58:56.0408141Z         >>> lib.full(x, 3.14)
2025-03-04T20:58:56.0408436Z     
2025-03-04T20:58:56.0408984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:56.0409827Z 
2025-03-04T20:58:56.0633629Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202.
2025-03-04T20:58:56.0634754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:56.0635335Z Return the sum of each row of the given sparse tensor.
2025-03-04T20:58:56.0635618Z 
2025-03-04T20:58:56.0635870Z     Returns the sum of each row of the sparse tensor :attr:`input` in the given
2025-03-04T20:58:56.0636439Z     dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2025-03-04T20:58:56.0636983Z     reduce over all of them. When sum over all ``sparse_dim``, this method
2025-03-04T20:58:56.0637490Z     returns a dense tensor instead of a sparse tensor.
2025-03-04T20:58:56.0637761Z 
2025-03-04T20:58:56.0638038Z     All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2025-03-04T20:58:56.0638640Z     tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2025-03-04T20:58:56.0638962Z 
2025-03-04T20:58:56.0639208Z     During backward, only gradients at ``nnz`` locations of :attr:`input`
2025-03-04T20:58:56.0639814Z     will propagate back. Note that the gradients of :attr:`input` is coalesced.
2025-03-04T20:58:56.0640175Z 
2025-03-04T20:58:56.0640283Z     Args:
2025-03-04T20:58:56.0640542Z         input (Tensor): the input sparse tensor
2025-03-04T20:58:56.0641111Z         dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2025-03-04T20:58:56.0641624Z             over all dims.
2025-03-04T20:58:56.0642070Z         dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2025-03-04T20:58:56.0642570Z             Default: dtype of :attr:`input`.
2025-03-04T20:58:56.0642813Z 
2025-03-04T20:58:56.0642929Z     Example::
2025-03-04T20:58:56.0643086Z 
2025-03-04T20:58:56.0643181Z         >>> nnz = 3
2025-03-04T20:58:56.0643441Z         >>> dims = [5, 5, 2, 3]
2025-03-04T20:58:56.0643808Z         >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2025-03-04T20:58:56.0644286Z                            torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2025-03-04T20:58:56.0644745Z         >>> V = torch.randn(nnz, dims[2], dims[3])
2025-03-04T20:58:56.0645112Z         >>> size = torch.Size(dims)
2025-03-04T20:58:56.0645480Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:58:56.0645882Z         >>> S = torch.sparse_coo_tensor(I, V, size)
2025-03-04T20:58:56.0646230Z         >>> S
2025-03-04T20:58:56.0646494Z         tensor(indices=tensor([[2, 0, 3],
2025-03-04T20:58:56.0646838Z                                [2, 4, 1]]),
2025-03-04T20:58:56.0647206Z                values=tensor([[[-0.6438, -1.6467,  1.4004],
2025-03-04T20:58:56.0647594Z                                [ 0.3411,  0.0918, -0.2312]],
2025-03-04T20:58:56.0647906Z 
2025-03-04T20:58:56.0648039Z                               [[ 0.5348,  0.0634, -2.0494],
2025-03-04T20:58:56.0648403Z                                [-0.7125, -1.0646,  2.1844]],
2025-03-04T20:58:56.0648635Z 
2025-03-04T20:58:56.0648770Z                               [[ 0.1276,  0.1874, -0.6334],
2025-03-04T20:58:56.0649138Z                                [-1.9682, -0.5340,  0.7483]]]),
2025-03-04T20:58:56.0649541Z                size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2025-03-04T20:58:56.0649808Z 
2025-03-04T20:58:56.0650021Z         # when sum over only part of sparse_dims, return a sparse tensor
2025-03-04T20:58:56.0650466Z         >>> torch.sparse.sum(S, [1, 3])
2025-03-04T20:58:56.0650822Z         tensor(indices=tensor([[0, 2, 3]]),
2025-03-04T20:58:56.0651182Z                values=tensor([[-1.4512,  0.4073],
2025-03-04T20:58:56.0651523Z                               [-0.8901,  0.2017],
2025-03-04T20:58:56.0651866Z                               [-0.3183, -1.7539]]),
2025-03-04T20:58:56.0652248Z                size=(5, 2), nnz=3, layout=torch.sparse_coo)
2025-03-04T20:58:56.0652519Z 
2025-03-04T20:58:56.0652739Z         # when sum over all sparse dim, return a dense tensor
2025-03-04T20:58:56.0653137Z         # with summed dims squeezed
2025-03-04T20:58:56.0653481Z         >>> torch.sparse.sum(S, [0, 1, 3])
2025-03-04T20:58:56.0653895Z         tensor([-2.6596, -1.1450])
2025-03-04T20:58:56.0654198Z     
2025-03-04T20:58:56.0654589Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:56.0654979Z 
2025-03-04T20:58:56.6517595Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39.
2025-03-04T20:58:56.6518516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:56.6518923Z 
2025-03-04T20:58:56.6519154Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2025-03-04T20:58:56.6519710Z maps ``func`` over some dimension of the inputs. Semantically, vmap
2025-03-04T20:58:56.6520280Z pushes the map into PyTorch operations called by ``func``, effectively
2025-03-04T20:58:56.6520755Z vectorizing those operations.
2025-03-04T20:58:56.6520957Z 
2025-03-04T20:58:56.6521193Z vmap is useful for handling batch dimensions: one can write a function
2025-03-04T20:58:56.6521759Z ``func`` that runs on examples and then lift it to a function that can
2025-03-04T20:58:56.6522310Z take batches of examples with ``vmap(func)``. vmap can also be used to
2025-03-04T20:58:56.6522833Z compute batched gradients when composed with autograd.
2025-03-04T20:58:56.6523121Z 
2025-03-04T20:58:56.6523252Z .. note::
2025-03-04T20:58:56.6523583Z     :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2025-03-04T20:58:56.6524034Z     convenience. Use whichever one you'd like.
2025-03-04T20:58:56.6524281Z 
2025-03-04T20:58:56.6524383Z Args:
2025-03-04T20:58:56.6524735Z     func (function): A Python function that takes one or more arguments.
2025-03-04T20:58:56.6525199Z         Must return one or more Tensors.
2025-03-04T20:58:56.6525645Z     in_dims (int or nested structure): Specifies which dimension of the
2025-03-04T20:58:56.6526154Z         inputs should be mapped over. ``in_dims`` should have a
2025-03-04T20:58:56.6526663Z         structure like the inputs. If the ``in_dim`` for a particular
2025-03-04T20:58:56.6527189Z         input is None, then that indicates there is no map dimension.
2025-03-04T20:58:56.6527611Z         Default: 0.
2025-03-04T20:58:56.6528025Z     out_dims (int or Tuple[int]): Specifies where the mapped dimension
2025-03-04T20:58:56.6528570Z         should appear in the outputs. If ``out_dims`` is a Tuple, then
2025-03-04T20:58:56.6529057Z         it should have one element per output. Default: 0.
2025-03-04T20:58:56.6529534Z     randomness (str): Specifies whether the randomness in this
2025-03-04T20:58:56.6530070Z         vmap should be the same or different across batches. If 'different',
2025-03-04T20:58:56.6530853Z         the randomness for each batch will be different. If 'same', the
2025-03-04T20:58:56.6531408Z         randomness will be the same across batches. If 'error', any calls to
2025-03-04T20:58:56.6531977Z         random functions will error. Default: 'error'. WARNING: this flag
2025-03-04T20:58:56.6532535Z         only applies to random PyTorch operations and does not apply to
2025-03-04T20:58:56.6533024Z         Python's random module or numpy randomness.
2025-03-04T20:58:56.6533511Z     chunk_size (None or int): If None (default), apply a single vmap over inputs.
2025-03-04T20:58:56.6534100Z         If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2025-03-04T20:58:56.6534721Z         Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2025-03-04T20:58:56.6535388Z         If you run into memory issues computing the vmap, please try a non-None chunk_size.
2025-03-04T20:58:56.6535794Z 
2025-03-04T20:58:56.6535890Z Returns:
2025-03-04T20:58:56.6536236Z     Returns a new "batched" function. It takes the same inputs as
2025-03-04T20:58:56.6536743Z     ``func``, except each input has an extra dimension at the index
2025-03-04T20:58:56.6537329Z     specified by ``in_dims``. It takes returns the same outputs as
2025-03-04T20:58:56.6537932Z     ``func``, except each output has an extra dimension at the index
2025-03-04T20:58:56.6538485Z     specified by ``out_dims``.
2025-03-04T20:58:56.6538703Z 
2025-03-04T20:58:56.6538803Z .. warning:
2025-03-04T20:58:56.6539157Z     :func:`vmap` works best with functional-style code. Please do not
2025-03-04T20:58:56.6539682Z     perform any side-effects in ``func``, with the exception of
2025-03-04T20:58:56.6540247Z     in-place PyTorch operations. Examples of side-effects include mutating
2025-03-04T20:58:56.6540858Z     Python data structures and assigning values to variables not captured
2025-03-04T20:58:56.6541317Z     in ``func``.
2025-03-04T20:58:56.6541465Z 
2025-03-04T20:58:56.6541721Z One example of using :func:`vmap` is to compute batched dot products. PyTorch
2025-03-04T20:58:56.6542330Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2025-03-04T20:58:56.6542912Z rummaging through docs, use :func:`vmap` to construct a new function.
2025-03-04T20:58:56.6543268Z 
2025-03-04T20:58:56.6543440Z     >>> torch.dot                            # [D], [D] -> []
2025-03-04T20:58:56.6543935Z     >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2025-03-04T20:58:56.6544413Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-04T20:58:56.6544781Z     >>> batched_dot(x, y)
2025-03-04T20:58:56.6544959Z 
2025-03-04T20:58:56.6545213Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2025-03-04T20:58:56.6545700Z model authoring experience.
2025-03-04T20:58:56.6545894Z 
2025-03-04T20:58:56.6546032Z     >>> batch_size, feature_size = 3, 5
2025-03-04T20:58:56.6546453Z     >>> weights = torch.randn(feature_size, requires_grad=True)
2025-03-04T20:58:56.6546856Z     >>>
2025-03-04T20:58:56.6547106Z     >>> def model(feature_vec):
2025-03-04T20:58:56.6547458Z     >>>     # Very simple linear model with activation
2025-03-04T20:58:56.6547859Z     >>>     return feature_vec.dot(weights).relu()
2025-03-04T20:58:56.6548195Z     >>>
2025-03-04T20:58:56.6548491Z     >>> examples = torch.randn(batch_size, feature_size)
2025-03-04T20:58:56.6548896Z     >>> result = torch.vmap(model)(examples)
2025-03-04T20:58:56.6549147Z 
2025-03-04T20:58:56.6549406Z :func:`vmap` can also help vectorize computations that were previously difficult
2025-03-04T20:58:56.6550073Z or impossible to batch. One example is higher-order gradient computation.
2025-03-04T20:58:56.6550678Z The PyTorch autograd engine computes vjps (vector-Jacobian products).
2025-03-04T20:58:56.6551273Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2025-03-04T20:58:56.6551888Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2025-03-04T20:58:56.6552510Z we can vectorize the whole computation, computing the Jacobian in a single
2025-03-04T20:58:56.6553040Z call to ``autograd.grad``.
2025-03-04T20:58:56.6553242Z 
2025-03-04T20:58:56.6553337Z     >>> # Setup
2025-03-04T20:58:56.6553582Z     >>> N = 5
2025-03-04T20:58:56.6553832Z     >>> f = lambda x: x ** 2
2025-03-04T20:58:56.6554155Z     >>> x = torch.randn(N, requires_grad=True)
2025-03-04T20:58:56.6554502Z     >>> y = f(x)
2025-03-04T20:58:56.6554767Z     >>> I_N = torch.eye(N)
2025-03-04T20:58:56.6555048Z     >>>
2025-03-04T20:58:56.6555293Z     >>> # Sequential approach
2025-03-04T20:58:56.6555720Z     >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2025-03-04T20:58:56.6556194Z     >>>                  for v in I_N.unbind()]
2025-03-04T20:58:56.6556553Z     >>> jacobian = torch.stack(jacobian_rows)
2025-03-04T20:58:56.6556894Z     >>>
2025-03-04T20:58:56.6557151Z     >>> # vectorized gradient computation
2025-03-04T20:58:56.6557491Z     >>> def get_vjp(v):
2025-03-04T20:58:56.6557799Z     >>>     return torch.autograd.grad(y, x, v)
2025-03-04T20:58:56.6558180Z     >>> jacobian = torch.vmap(get_vjp)(I_N)
2025-03-04T20:58:56.6558470Z 
2025-03-04T20:58:56.6558743Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2025-03-04T20:58:56.6559146Z 
2025-03-04T20:58:56.6559301Z     >>> torch.dot                            # [D], [D] -> []
2025-03-04T20:58:56.6559920Z     >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2025-03-04T20:58:56.6560476Z     >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2025-03-04T20:58:56.6560879Z     >>> batched_dot(x, y) # tensor of size [2, 3]
2025-03-04T20:58:56.6561136Z 
2025-03-04T20:58:56.6561388Z If the inputs are not batched along the first dimension, ``in_dims`` specifies
2025-03-04T20:58:56.6561933Z the dimension that each inputs are batched along as
2025-03-04T20:58:56.6562221Z 
2025-03-04T20:58:56.6562397Z     >>> torch.dot                            # [N], [N] -> []
2025-03-04T20:58:56.6562897Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2025-03-04T20:58:56.6563387Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-04T20:58:56.6563893Z     >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension
2025-03-04T20:58:56.6564262Z 
2025-03-04T20:58:56.6564546Z If there are multiple inputs each of which is batched along different dimensions,
2025-03-04T20:58:56.6565145Z ``in_dims`` must be a tuple with the batch dimension for each input as
2025-03-04T20:58:56.6565466Z 
2025-03-04T20:58:56.6565627Z     >>> torch.dot                            # [D], [D] -> []
2025-03-04T20:58:56.6566142Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2025-03-04T20:58:56.6566639Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-04T20:58:56.6567135Z     >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
2025-03-04T20:58:56.6567504Z 
2025-03-04T20:58:56.6567763Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2025-03-04T20:58:56.6568255Z matching the shape of the input:
2025-03-04T20:58:56.6568465Z 
2025-03-04T20:58:56.6568625Z     >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
2025-03-04T20:58:56.6569024Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-04T20:58:56.6569387Z     >>> input = {'x': x, 'y': y}
2025-03-04T20:58:56.6569779Z     >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
2025-03-04T20:58:56.6570191Z     >>> batched_dot(input)
2025-03-04T20:58:56.6570372Z 
2025-03-04T20:58:56.6570668Z By default, the output is batched along the first dimension. However, it can be batched
2025-03-04T20:58:56.6571194Z along any dimension by using ``out_dims``
2025-03-04T20:58:56.6571440Z 
2025-03-04T20:58:56.6571547Z     >>> f = lambda x: x ** 2
2025-03-04T20:58:56.6571846Z     >>> x = torch.randn(2, 5)
2025-03-04T20:58:56.6572176Z     >>> batched_pow = torch.vmap(f, out_dims=1)
2025-03-04T20:58:56.6572579Z     >>> batched_pow(x) # [5, 2]
2025-03-04T20:58:56.6572785Z 
2025-03-04T20:58:56.6573082Z For any function that uses kwargs, the returned function will not batch the kwargs but will
2025-03-04T20:58:56.6573855Z accept kwargs
2025-03-04T20:58:56.6574019Z 
2025-03-04T20:58:56.6574129Z     >>> x = torch.randn([2, 5])
2025-03-04T20:58:56.6574441Z     >>> def fn(x, scale=4.):
2025-03-04T20:58:56.6574746Z     >>>   return x * scale
2025-03-04T20:58:56.6575026Z     >>>
2025-03-04T20:58:56.6575276Z     >>> batched_pow = torch.vmap(fn)
2025-03-04T20:58:56.6575655Z     >>> assert torch.allclose(batched_pow(x), x * 4)
2025-03-04T20:58:56.6576155Z     >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
2025-03-04T20:58:56.6576507Z 
2025-03-04T20:58:56.6576629Z .. note::
2025-03-04T20:58:56.6576994Z     vmap does not provide general autobatching or handle variable-length
2025-03-04T20:58:56.6577462Z     sequences out of the box.
2025-03-04T20:58:56.6577657Z 
2025-03-04T20:58:56.6577991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:56.6578370Z 
2025-03-04T20:58:58.2029204Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=21.
2025-03-04T20:58:58.2030141Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2031093Z Create a custom operator whose implementation is backed by 1+ triton kernels.
2025-03-04T20:58:58.2031497Z 
2025-03-04T20:58:58.2031718Z     This is a more structured way of using triton kernels with PyTorch.
2025-03-04T20:58:58.2032327Z     Prefer using triton kernels with no ``torch.library`` custom operator wrappers
2025-03-04T20:58:58.2032985Z     (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because
2025-03-04T20:58:58.2033479Z     that is simpler;
2025-03-04T20:58:58.2033915Z     only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you
2025-03-04T20:58:58.2034585Z     want to create an operator that behaves like PyTorch built-in operators.
2025-03-04T20:58:58.2035421Z     For example, you may use a ``torch.library`` wrapper API to define the
2025-03-04T20:58:58.2036391Z     behavior of the triton kernel when passed a tensor subclass or under
2025-03-04T20:58:58.2037087Z     a TorchDispatchMode.
2025-03-04T20:58:58.2037284Z 
2025-03-04T20:58:58.2037555Z     Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op`
2025-03-04T20:58:58.2038054Z     when the implementation
2025-03-04T20:58:58.2038476Z     consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2025-03-04T20:58:58.2039005Z     custom operators as opaque (:func:`torch.compile` and
2025-03-04T20:58:58.2039544Z     :func:`torch.export.export` will never trace into them), but ``triton_op``
2025-03-04T20:58:58.2040141Z     makes the implementation visible to these subsystems, allowing them
2025-03-04T20:58:58.2040614Z     to optimize the triton kernel(s).
2025-03-04T20:58:58.2040841Z 
2025-03-04T20:58:58.2041051Z     Note that ``fn`` must only consist of calls to PyTorch-understood
2025-03-04T20:58:58.2041609Z     operators and triton kernels. Any triton kernels called inside ``fn``
2025-03-04T20:58:58.2042173Z     must be wrapped in a call to :func:`torch.library.wrap_triton`.
2025-03-04T20:58:58.2042486Z 
2025-03-04T20:58:58.2042592Z     Args:
2025-03-04T20:58:58.2042992Z         name (str): A name for the custom op that looks like "{namespace}::{name}",
2025-03-04T20:58:58.2043571Z             e.g. "mylib::my_linear". The name is used as the op's stable identifier
2025-03-04T20:58:58.2044094Z             in PyTorch subsystems (e.g. torch.export, FX graphs).
2025-03-04T20:58:58.2044632Z             To avoid name collisions, please use your project name as the namespace;
2025-03-04T20:58:58.2045220Z             e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2025-03-04T20:58:58.2045842Z         mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2025-03-04T20:58:58.2046608Z             This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2025-03-04T20:58:58.2047349Z             it pessimistically assumes that all inputs to the operator are being mutated.
2025-03-04T20:58:58.2047945Z         schema (None | str): A schema string for the operator. If None
2025-03-04T20:58:58.2048490Z             (recommended) we'll infer a schema for the operator from its type
2025-03-04T20:58:58.2049033Z             annotations. We recommend letting us infer a schema unless you
2025-03-04T20:58:58.2049496Z             have a specific reason not to.
2025-03-04T20:58:58.2049901Z             Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2025-03-04T20:58:58.2050183Z 
2025-03-04T20:58:58.2050286Z     Example::
2025-03-04T20:58:58.2050438Z 
2025-03-04T20:58:58.2050587Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:58:58.2050956Z         >>> import torch
2025-03-04T20:58:58.2051303Z         >>> from torch.library import triton_op, wrap_triton
2025-03-04T20:58:58.2051680Z         >>>
2025-03-04T20:58:58.2051920Z         >>> import triton
2025-03-04T20:58:58.2052309Z         >>> from triton import language as tl
2025-03-04T20:58:58.2052654Z         >>>
2025-03-04T20:58:58.2052898Z         >>> @triton.jit
2025-03-04T20:58:58.2053185Z         >>> def add_kernel(
2025-03-04T20:58:58.2053545Z         >>>     in_ptr0,
2025-03-04T20:58:58.2053825Z         >>>     in_ptr1,
2025-03-04T20:58:58.2054096Z         >>>     out_ptr,
2025-03-04T20:58:58.2054372Z         >>>     n_elements,
2025-03-04T20:58:58.2054684Z         >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-04T20:58:58.2055004Z         >>> ):
2025-03-04T20:58:58.2055270Z         >>>     pid = tl.program_id(axis=0)
2025-03-04T20:58:58.2055628Z         >>>     block_start = pid * BLOCK_SIZE
2025-03-04T20:58:58.2056026Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-04T20:58:58.2056423Z         >>>     mask = offsets < n_elements
2025-03-04T20:58:58.2056795Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-04T20:58:58.2057229Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-04T20:58:58.2057594Z         >>>     output = x + y
2025-03-04T20:58:58.2058029Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-04T20:58:58.2058399Z         >>>
2025-03-04T20:58:58.2058688Z         >>> @triton_op("mylib::add", mutates_args={})
2025-03-04T20:58:58.2059142Z         >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2025-03-04T20:58:58.2059578Z         >>>     output = torch.empty_like(x)
2025-03-04T20:58:58.2059943Z         >>>     n_elements = output.numel()
2025-03-04T20:58:58.2060270Z         >>>
2025-03-04T20:58:58.2060511Z         >>>     def grid(meta):
2025-03-04T20:58:58.2060888Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-04T20:58:58.2061282Z         >>>
2025-03-04T20:58:58.2061608Z         >>>     # NB: we need to wrap the triton kernel in a call to wrap_triton
2025-03-04T20:58:58.2062120Z         >>>     wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2025-03-04T20:58:58.2062545Z         >>>     return output
2025-03-04T20:58:58.2062830Z         >>>
2025-03-04T20:58:58.2063076Z         >>> @torch.compile
2025-03-04T20:58:58.2063352Z         >>> def f(x, y):
2025-03-04T20:58:58.2063644Z         >>>     return add(x, y)
2025-03-04T20:58:58.2063990Z         >>>
2025-03-04T20:58:58.2064257Z         >>> x = torch.randn(3, device="cuda")
2025-03-04T20:58:58.2064623Z         >>> y = torch.randn(3, device="cuda")
2025-03-04T20:58:58.2064959Z         >>>
2025-03-04T20:58:58.2065191Z         >>> z = f(x, y)
2025-03-04T20:58:58.2065482Z         >>> assert torch.allclose(z, x + y)
2025-03-04T20:58:58.2065724Z 
2025-03-04T20:58:58.2065815Z     
2025-03-04T20:58:58.2066202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.2066579Z 
2025-03-04T20:58:58.2067102Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=202.
2025-03-04T20:58:58.2068073Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2068671Z Allows capture of a triton kernel into a graph via make_fx or
2025-03-04T20:58:58.2069112Z     non-strict ``torch.export``.
2025-03-04T20:58:58.2069339Z 
2025-03-04T20:58:58.2069534Z     These technologies perform Dispatcher-based tracing (via
2025-03-04T20:58:58.2070060Z     ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2025-03-04T20:58:58.2070600Z     The ``wrap_triton`` API wraps a triton kernel into a callable that
2025-03-04T20:58:58.2071058Z     can actually be traced into a graph.
2025-03-04T20:58:58.2071307Z 
2025-03-04T20:58:58.2071551Z     Please use this API together with :func:`torch.library.triton_op`.
2025-03-04T20:58:58.2071907Z 
2025-03-04T20:58:58.2072007Z     Examples:
2025-03-04T20:58:58.2072167Z 
2025-03-04T20:58:58.2072279Z         >>> # xdoctest: +SKIP
2025-03-04T20:58:58.2072587Z         >>> import torch
2025-03-04T20:58:58.2072877Z         >>> import triton
2025-03-04T20:58:58.2073232Z         >>> from triton import language as tl
2025-03-04T20:58:58.2073873Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-04T20:58:58.2074432Z         >>> from torch.library import wrap_triton
2025-03-04T20:58:58.2074789Z         >>>
2025-03-04T20:58:58.2075033Z         >>> @triton.jit
2025-03-04T20:58:58.2075310Z         >>> def add_kernel(
2025-03-04T20:58:58.2075599Z         >>>     in_ptr0,
2025-03-04T20:58:58.2075875Z         >>>     in_ptr1,
2025-03-04T20:58:58.2076133Z         >>>     out_ptr,
2025-03-04T20:58:58.2076409Z         >>>     n_elements,
2025-03-04T20:58:58.2076714Z         >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-04T20:58:58.2077045Z         >>> ):
2025-03-04T20:58:58.2077312Z         >>>     pid = tl.program_id(axis=0)
2025-03-04T20:58:58.2077669Z         >>>     block_start = pid * BLOCK_SIZE
2025-03-04T20:58:58.2078072Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-04T20:58:58.2078470Z         >>>     mask = offsets < n_elements
2025-03-04T20:58:58.2078845Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-04T20:58:58.2079244Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-04T20:58:58.2079610Z         >>>     output = x + y
2025-03-04T20:58:58.2079960Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-04T20:58:58.2080327Z         >>>
2025-03-04T20:58:58.2080570Z         >>> def add(x, y):
2025-03-04T20:58:58.2080876Z         >>>     output = torch.empty_like(x)
2025-03-04T20:58:58.2081239Z         >>>     n_elements = output.numel()
2025-03-04T20:58:58.2081568Z         >>>
2025-03-04T20:58:58.2081802Z         >>>     def grid_fn(meta):
2025-03-04T20:58:58.2082196Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-04T20:58:58.2082593Z         >>>
2025-03-04T20:58:58.2082942Z         >>>     wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2025-03-04T20:58:58.2083379Z         >>>     return output
2025-03-04T20:58:58.2083670Z         >>>
2025-03-04T20:58:58.2083929Z         >>> x = torch.randn(3, device="cuda")
2025-03-04T20:58:58.2084295Z         >>> y = torch.randn(3, device="cuda")
2025-03-04T20:58:58.2084647Z         >>> gm = make_fx(add)(x, y)
2025-03-04T20:58:58.2084975Z         >>> print(gm.code)
2025-03-04T20:58:58.2085282Z         >>> # def forward(self, x_1, y_1):
2025-03-04T20:58:58.2085761Z         >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2025-03-04T20:58:58.2086392Z         >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2025-03-04T20:58:58.2086906Z         >>> #         kernel_idx = 0, constant_args_idx = 0,
2025-03-04T20:58:58.2087284Z         >>> #         grid = [(1, 1, 1)], kwargs = {
2025-03-04T20:58:58.2087687Z         >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2025-03-04T20:58:58.2088175Z         >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2025-03-04T20:58:58.2088537Z         >>> #         })
2025-03-04T20:58:58.2088827Z         >>> #     return empty_like
2025-03-04T20:58:58.2089036Z 
2025-03-04T20:58:58.2089140Z     
2025-03-04T20:58:58.2089525Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.2089919Z 
2025-03-04T20:58:58.2878117Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=326.
2025-03-04T20:58:58.2879295Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2879884Z 
2025-03-04T20:58:58.2880267Z Raises an AssertionError if two items are not equal up to desired
2025-03-04T20:58:58.2881004Z precision.
2025-03-04T20:58:58.2881139Z 
2025-03-04T20:58:58.2881360Z .. note:: It is recommended to use one of `assert_allclose`,
2025-03-04T20:58:58.2881862Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-04T20:58:58.2882368Z           instead of this function for more consistent floating point
2025-03-04T20:58:58.2882947Z           comparisons.
2025-03-04T20:58:58.2883129Z 
2025-03-04T20:58:58.2883354Z The test verifies that the elements of `actual` and `desired` satisfy.
2025-03-04T20:58:58.2883708Z 
2025-03-04T20:58:58.2883989Z     ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2025-03-04T20:58:58.2884413Z 
2025-03-04T20:58:58.2884776Z That is a looser test than originally documented, but agrees with what the
2025-03-04T20:58:58.2885755Z actual implementation in `assert_array_almost_equal` did up to rounding
2025-03-04T20:58:58.2886785Z vagaries. An exception is raised at conflicting values. For ndarrays this
2025-03-04T20:58:58.2887286Z delegates to assert_array_almost_equal
2025-03-04T20:58:58.2887527Z 
2025-03-04T20:58:58.2887628Z Parameters
2025-03-04T20:58:58.2887870Z ----------
2025-03-04T20:58:58.2888119Z actual : array_like
2025-03-04T20:58:58.2888400Z     The object to check.
2025-03-04T20:58:58.2888686Z desired : array_like
2025-03-04T20:58:58.2888968Z     The expected object.
2025-03-04T20:58:58.2889255Z decimal : int, optional
2025-03-04T20:58:58.2889570Z     Desired precision, default is 7.
2025-03-04T20:58:58.2890025Z err_msg : str, optional
2025-03-04T20:58:58.2890483Z     The error message to be printed in case of failure.
2025-03-04T20:58:58.2890943Z verbose : bool, optional
2025-03-04T20:58:58.2891343Z     If True, the conflicting values are appended to the error message.
2025-03-04T20:58:58.2891672Z 
2025-03-04T20:58:58.2891779Z Raises
2025-03-04T20:58:58.2891993Z ------
2025-03-04T20:58:58.2892233Z AssertionError
2025-03-04T20:58:58.2892591Z   If actual and desired are not equal up to specified precision.
2025-03-04T20:58:58.2892921Z 
2025-03-04T20:58:58.2893016Z See Also
2025-03-04T20:58:58.2893243Z --------
2025-03-04T20:58:58.2893625Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:58:58.2894140Z                  relative and/or absolute precision.
2025-03-04T20:58:58.2894614Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:58:58.2894962Z 
2025-03-04T20:58:58.2895055Z Examples
2025-03-04T20:58:58.2895281Z --------
2025-03-04T20:58:58.2895591Z >>> from torch._numpy.testing import assert_almost_equal
2025-03-04T20:58:58.2896031Z >>> assert_almost_equal(2.3333333333333, 2.33333334)
2025-03-04T20:58:58.2896474Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2025-03-04T20:58:58.2896987Z Traceback (most recent call last):
2025-03-04T20:58:58.2897301Z     ...
2025-03-04T20:58:58.2897535Z AssertionError:
2025-03-04T20:58:58.2897917Z Arrays are not almost equal to 10 decimals
2025-03-04T20:58:58.2898265Z  ACTUAL: 2.3333333333333
2025-03-04T20:58:58.2898532Z  DESIRED: 2.33333334
2025-03-04T20:58:58.2898705Z 
2025-03-04T20:58:58.2898862Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
2025-03-04T20:58:58.2899436Z ...                     np.array([1.0,2.33333334]), decimal=9)
2025-03-04T20:58:58.2899810Z Traceback (most recent call last):
2025-03-04T20:58:58.2900128Z     ...
2025-03-04T20:58:58.2900387Z AssertionError:
2025-03-04T20:58:58.2900675Z Arrays are not almost equal to 9 decimals
2025-03-04T20:58:58.2901013Z <BLANKLINE>
2025-03-04T20:58:58.2901270Z Mismatched elements: 1 / 2 (50%)
2025-03-04T20:58:58.2901624Z Max absolute difference: 6.666699636781459e-09
2025-03-04T20:58:58.2902016Z Max relative difference: 2.8571569790287484e-09
2025-03-04T20:58:58.2902423Z  x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-04T20:58:58.2902832Z  y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-04T20:58:58.2903087Z 
2025-03-04T20:58:58.2903091Z 
2025-03-04T20:58:58.2903370Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.2903749Z 
2025-03-04T20:58:58.2904338Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=451.
2025-03-04T20:58:58.2905297Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2905732Z 
2025-03-04T20:58:58.2905976Z Raises an AssertionError if two items are not equal up to significant
2025-03-04T20:58:58.2906424Z digits.
2025-03-04T20:58:58.2906549Z 
2025-03-04T20:58:58.2906833Z .. note:: It is recommended to use one of `assert_allclose`,
2025-03-04T20:58:58.2907321Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-04T20:58:58.2907830Z           instead of this function for more consistent floating point
2025-03-04T20:58:58.2908252Z           comparisons.
2025-03-04T20:58:58.2908421Z 
2025-03-04T20:58:58.2908628Z Given two numbers, check that they are approximately equal.
2025-03-04T20:58:58.2909180Z Approximately equal is defined as the number of significant digits
2025-03-04T20:58:58.2909632Z that agree.
2025-03-04T20:58:58.2909767Z 
2025-03-04T20:58:58.2909882Z Parameters
2025-03-04T20:58:58.2910106Z ----------
2025-03-04T20:58:58.2910342Z actual : scalar
2025-03-04T20:58:58.2910604Z     The object to check.
2025-03-04T20:58:58.2910889Z desired : scalar
2025-03-04T20:58:58.2911152Z     The expected object.
2025-03-04T20:58:58.2911444Z significant : int, optional
2025-03-04T20:58:58.2911763Z     Desired precision, default is 7.
2025-03-04T20:58:58.2912095Z err_msg : str, optional
2025-03-04T20:58:58.2912427Z     The error message to be printed in case of failure.
2025-03-04T20:58:58.2912810Z verbose : bool, optional
2025-03-04T20:58:58.2913210Z     If True, the conflicting values are appended to the error message.
2025-03-04T20:58:58.2913538Z 
2025-03-04T20:58:58.2913640Z Raises
2025-03-04T20:58:58.2913863Z ------
2025-03-04T20:58:58.2914091Z AssertionError
2025-03-04T20:58:58.2914444Z   If actual and desired are not equal up to specified precision.
2025-03-04T20:58:58.2914759Z 
2025-03-04T20:58:58.2914863Z See Also
2025-03-04T20:58:58.2915076Z --------
2025-03-04T20:58:58.2915456Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:58:58.2915958Z                  relative and/or absolute precision.
2025-03-04T20:58:58.2916432Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:58:58.2916776Z 
2025-03-04T20:58:58.2916869Z Examples
2025-03-04T20:58:58.2917092Z --------
2025-03-04T20:58:58.2917508Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)  # doctest: +SKIP
2025-03-04T20:58:58.2918216Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,  # doctest: +SKIP
2025-03-04T20:58:58.2918729Z ...                                significant=8)
2025-03-04T20:58:58.2919233Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,  # doctest: +SKIP
2025-03-04T20:58:58.2919743Z ...                                significant=8)
2025-03-04T20:58:58.2920106Z Traceback (most recent call last):
2025-03-04T20:58:58.2920462Z     ...
2025-03-04T20:58:58.2920703Z AssertionError:
2025-03-04T20:58:58.2921054Z Items are not equal to 8 significant digits:
2025-03-04T20:58:58.2921412Z  ACTUAL: 1.234567e-21
2025-03-04T20:58:58.2921686Z  DESIRED: 1.2345672e-21
2025-03-04T20:58:58.2921856Z 
2025-03-04T20:58:58.2922035Z the evaluated condition that raises the exception is
2025-03-04T20:58:58.2922316Z 
2025-03-04T20:58:58.2922511Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
2025-03-04T20:58:58.2922902Z True
2025-03-04T20:58:58.2923023Z 
2025-03-04T20:58:58.2923027Z 
2025-03-04T20:58:58.2923301Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.2923679Z 
2025-03-04T20:58:58.2924263Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=730.
2025-03-04T20:58:58.2925219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2925607Z 
2025-03-04T20:58:58.2925837Z Raises an AssertionError if two array_like objects are not equal.
2025-03-04T20:58:58.2926169Z 
2025-03-04T20:58:58.2926413Z Given two array_like objects, check that the shape is equal and all
2025-03-04T20:58:58.2927016Z elements of these objects are equal (but see the Notes for the special
2025-03-04T20:58:58.2927583Z handling of a scalar). An exception is raised at shape mismatch or
2025-03-04T20:58:58.2928283Z conflicting values. In contrast to the standard usage in numpy, NaNs
2025-03-04T20:58:58.2928874Z are compared like numbers, no assertion is raised if both objects have
2025-03-04T20:58:58.2929345Z NaNs in the same positions.
2025-03-04T20:58:58.2929550Z 
2025-03-04T20:58:58.2929789Z The usual caution for verifying equality with floating point numbers is
2025-03-04T20:58:58.2930250Z advised.
2025-03-04T20:58:58.2930396Z 
2025-03-04T20:58:58.2930494Z Parameters
2025-03-04T20:58:58.2930730Z ----------
2025-03-04T20:58:58.2930969Z x : array_like
2025-03-04T20:58:58.2931235Z     The actual object to check.
2025-03-04T20:58:58.2931547Z y : array_like
2025-03-04T20:58:58.2931823Z     The desired, expected object.
2025-03-04T20:58:58.2932147Z err_msg : str, optional
2025-03-04T20:58:58.2932490Z     The error message to be printed in case of failure.
2025-03-04T20:58:58.2932910Z verbose : bool, optional
2025-03-04T20:58:58.2933310Z     If True, the conflicting values are appended to the error message.
2025-03-04T20:58:58.2933753Z strict : bool, optional
2025-03-04T20:58:58.2934140Z     If True, raise an AssertionError when either the shape or the data
2025-03-04T20:58:58.2934657Z     type of the array_like objects does not match. The special
2025-03-04T20:58:58.2935171Z     handling for scalars mentioned in the Notes section is disabled.
2025-03-04T20:58:58.2935493Z 
2025-03-04T20:58:58.2935584Z Raises
2025-03-04T20:58:58.2935806Z ------
2025-03-04T20:58:58.2936034Z AssertionError
2025-03-04T20:58:58.2936328Z     If actual and desired objects are not equal.
2025-03-04T20:58:58.2936589Z 
2025-03-04T20:58:58.2936681Z See Also
2025-03-04T20:58:58.2936905Z --------
2025-03-04T20:58:58.2937286Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:58:58.2937869Z                  relative and/or absolute precision.
2025-03-04T20:58:58.2938346Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:58:58.2938691Z 
2025-03-04T20:58:58.2938784Z Notes
2025-03-04T20:58:58.2939007Z -----
2025-03-04T20:58:58.2939338Z When one of `x` and `y` is a scalar and the other is array_like, the
2025-03-04T20:58:58.2939885Z function checks that each element of the array_like object is equal to
2025-03-04T20:58:58.2940476Z the scalar. This behaviour can be disabled with the `strict` parameter.
2025-03-04T20:58:58.2941016Z 
2025-03-04T20:58:58.2941144Z Examples
2025-03-04T20:58:58.2941415Z --------
2025-03-04T20:58:58.2941689Z The first assert does not raise an exception:
2025-03-04T20:58:58.2941979Z 
2025-03-04T20:58:58.2942215Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
2025-03-04T20:58:58.2942638Z ...                               [np.exp(0),2.33333, np.nan])
2025-03-04T20:58:58.2943388Z 
2025-03-04T20:58:58.2943638Z Use `assert_allclose` or one of the nulp (number of floating point values)
2025-03-04T20:58:58.2944128Z functions for these cases instead:
2025-03-04T20:58:58.2944344Z 
2025-03-04T20:58:58.2944512Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
2025-03-04T20:58:58.2944916Z ...                            [1, np.sqrt(np.pi)**2, np.nan],
2025-03-04T20:58:58.2945321Z ...                            rtol=1e-10, atol=0)
2025-03-04T20:58:58.2945566Z 
2025-03-04T20:58:58.2945784Z As mentioned in the Notes section, `assert_array_equal` has special
2025-03-04T20:58:58.2946353Z handling for scalars. Here the test checks that each value in `x` is 3:
2025-03-04T20:58:58.2946716Z 
2025-03-04T20:58:58.2946832Z >>> x = np.full((2, 5), fill_value=3)
2025-03-04T20:58:58.2947178Z >>> np.testing.assert_array_equal(x, 3)
2025-03-04T20:58:58.2947419Z 
2025-03-04T20:58:58.2947646Z Use `strict` to raise an AssertionError when comparing a scalar with an
2025-03-04T20:58:58.2948088Z array:
2025-03-04T20:58:58.2948224Z 
2025-03-04T20:58:58.2948381Z >>> np.testing.assert_array_equal(x, 3, strict=True)
2025-03-04T20:58:58.2948817Z Traceback (most recent call last):
2025-03-04T20:58:58.2949131Z     ...
2025-03-04T20:58:58.2949365Z AssertionError:
2025-03-04T20:58:58.2949627Z Arrays are not equal
2025-03-04T20:58:58.2949967Z <BLANKLINE>
2025-03-04T20:58:58.2950215Z (shapes (2, 5), () mismatch)
2025-03-04T20:58:58.2950523Z  x: torch.ndarray([[3, 3, 3, 3, 3],
2025-03-04T20:58:58.2950842Z         [3, 3, 3, 3, 3]])
2025-03-04T20:58:58.2951123Z  y: torch.ndarray(3)
2025-03-04T20:58:58.2951285Z 
2025-03-04T20:58:58.2951521Z The `strict` parameter also ensures that the array data types match:
2025-03-04T20:58:58.2951854Z 
2025-03-04T20:58:58.2951978Z >>> x = np.array([2, 2, 2])
2025-03-04T20:58:58.2952302Z >>> y = np.array([2., 2., 2.], dtype=np.float32)
2025-03-04T20:58:58.2952696Z >>> np.testing.assert_array_equal(x, y, strict=True)
2025-03-04T20:58:58.2953093Z Traceback (most recent call last):
2025-03-04T20:58:58.2953401Z     ...
2025-03-04T20:58:58.2953631Z AssertionError:
2025-03-04T20:58:58.2953893Z Arrays are not equal
2025-03-04T20:58:58.2954158Z <BLANKLINE>
2025-03-04T20:58:58.2954447Z (dtypes dtype("int64"), dtype("float32") mismatch)
2025-03-04T20:58:58.2954832Z  x: torch.ndarray([2, 2, 2])
2025-03-04T20:58:58.2955143Z  y: torch.ndarray([2., 2., 2.])
2025-03-04T20:58:58.2955336Z 
2025-03-04T20:58:58.2955615Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.2955994Z 
2025-03-04T20:58:58.2956597Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=836.
2025-03-04T20:58:58.2957591Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2957980Z 
2025-03-04T20:58:58.2958218Z Raises an AssertionError if two objects are not equal up to desired
2025-03-04T20:58:58.2958669Z precision.
2025-03-04T20:58:58.2958803Z 
2025-03-04T20:58:58.2959004Z .. note:: It is recommended to use one of `assert_allclose`,
2025-03-04T20:58:58.2959493Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-04T20:58:58.2959999Z           instead of this function for more consistent floating point
2025-03-04T20:58:58.2960422Z           comparisons.
2025-03-04T20:58:58.2960589Z 
2025-03-04T20:58:58.2960848Z The test verifies identical shapes and that the elements of ``actual`` and
2025-03-04T20:58:58.2961320Z ``desired`` satisfy.
2025-03-04T20:58:58.2961484Z 
2025-03-04T20:58:58.2961635Z     ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2025-03-04T20:58:58.2961883Z 
2025-03-04T20:58:58.2962132Z That is a looser test than originally documented, but agrees with what the
2025-03-04T20:58:58.2962742Z actual implementation did up to rounding vagaries. An exception is raised
2025-03-04T20:58:58.2963366Z at shape mismatch or conflicting values. In contrast to the standard usage
2025-03-04T20:58:58.2964010Z in numpy, NaNs are compared like numbers, no assertion is raised if both
2025-03-04T20:58:58.2964503Z objects have NaNs in the same positions.
2025-03-04T20:58:58.2964738Z 
2025-03-04T20:58:58.2964853Z Parameters
2025-03-04T20:58:58.2965078Z ----------
2025-03-04T20:58:58.2965322Z x : array_like
2025-03-04T20:58:58.2965594Z     The actual object to check.
2025-03-04T20:58:58.2965903Z y : array_like
2025-03-04T20:58:58.2966177Z     The desired, expected object.
2025-03-04T20:58:58.2966508Z decimal : int, optional
2025-03-04T20:58:58.2966815Z     Desired precision, default is 6.
2025-03-04T20:58:58.2967159Z err_msg : str, optional
2025-03-04T20:58:58.2967502Z   The error message to be printed in case of failure.
2025-03-04T20:58:58.2967893Z verbose : bool, optional
2025-03-04T20:58:58.2968299Z     If True, the conflicting values are appended to the error message.
2025-03-04T20:58:58.2968626Z 
2025-03-04T20:58:58.2968733Z Raises
2025-03-04T20:58:58.2968956Z ------
2025-03-04T20:58:58.2969191Z AssertionError
2025-03-04T20:58:58.2969547Z     If actual and desired are not equal up to specified precision.
2025-03-04T20:58:58.2969901Z 
2025-03-04T20:58:58.2970003Z See Also
2025-03-04T20:58:58.2984660Z --------
2025-03-04T20:58:58.2985175Z assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:58:58.2985879Z                  relative and/or absolute precision.
2025-03-04T20:58:58.2986370Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:58:58.2986720Z 
2025-03-04T20:58:58.2986819Z Examples
2025-03-04T20:58:58.2987055Z --------
2025-03-04T20:58:58.2987333Z the first assert does not raise an exception
2025-03-04T20:58:58.2987583Z 
2025-03-04T20:58:58.2987827Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
2025-03-04T20:58:58.2988262Z ...                                      [1.0,2.333,np.nan])
2025-03-04T20:58:58.2988501Z 
2025-03-04T20:58:58.2988701Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-04T20:58:58.2989147Z ...                                      [1.0,2.33339,np.nan], decimal=5)
2025-03-04T20:58:58.2989537Z Traceback (most recent call last):
2025-03-04T20:58:58.2989853Z     ...
2025-03-04T20:58:58.2990091Z AssertionError:
2025-03-04T20:58:58.2990380Z Arrays are not almost equal to 5 decimals
2025-03-04T20:58:58.2990709Z <BLANKLINE>
2025-03-04T20:58:58.2990968Z Mismatched elements: 1 / 3 (33.3%)
2025-03-04T20:58:58.2991325Z Max absolute difference: 5.999999999994898e-05
2025-03-04T20:58:58.2991718Z Max relative difference: 2.5713661239633743e-05
2025-03-04T20:58:58.2992151Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-04T20:58:58.2992609Z  y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2025-03-04T20:58:58.2992901Z 
2025-03-04T20:58:58.2993090Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-04T20:58:58.2993527Z ...                                      [1.0,2.33333, 5], decimal=5)
2025-03-04T20:58:58.2993904Z Traceback (most recent call last):
2025-03-04T20:58:58.2994253Z     ...
2025-03-04T20:58:58.2994488Z AssertionError:
2025-03-04T20:58:58.2994773Z Arrays are not almost equal to 5 decimals
2025-03-04T20:58:58.2995116Z <BLANKLINE>
2025-03-04T20:58:58.2995370Z x and y nan location mismatch:
2025-03-04T20:58:58.2995743Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-04T20:58:58.2996197Z  y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2025-03-04T20:58:58.2996472Z 
2025-03-04T20:58:58.2996476Z 
2025-03-04T20:58:58.2996753Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.2997131Z 
2025-03-04T20:58:58.2997773Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1786.
2025-03-04T20:58:58.2998757Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.2999374Z Context manager that resets warning registry for catching warnings
2025-03-04T20:58:58.2999766Z 
2025-03-04T20:58:58.3000035Z     Warnings can be slippery, because, whenever a warning is triggered, Python
2025-03-04T20:58:58.3000647Z     adds a ``__warningregistry__`` member to the *calling* module.  This makes
2025-03-04T20:58:58.3001257Z     it impossible to retrigger the warning in this module, whatever you put in
2025-03-04T20:58:58.3001884Z     the warnings filters.  This context manager accepts a sequence of `modules`
2025-03-04T20:58:58.3002406Z     as a keyword argument to its constructor and:
2025-03-04T20:58:58.3002664Z 
2025-03-04T20:58:58.3002917Z     * stores and removes any ``__warningregistry__`` entries in given `modules`
2025-03-04T20:58:58.3003376Z       on entry;
2025-03-04T20:58:58.3003731Z     * resets ``__warningregistry__`` to its previous state on exit.
2025-03-04T20:58:58.3004043Z 
2025-03-04T20:58:58.3004287Z     This makes it possible to trigger any warning afresh inside the context
2025-03-04T20:58:58.3004837Z     manager without disturbing the state of warnings outside.
2025-03-04T20:58:58.3005145Z 
2025-03-04T20:58:58.3005403Z     For compatibility with Python 3.0, please consider all arguments to be
2025-03-04T20:58:58.3005927Z     keyword-only.
2025-03-04T20:58:58.3006082Z 
2025-03-04T20:58:58.3006196Z     Parameters
2025-03-04T20:58:58.3006437Z     ----------
2025-03-04T20:58:58.3006757Z     record : bool, optional
2025-03-04T20:58:58.3007153Z         Specifies whether warnings should be captured by a custom
2025-03-04T20:58:58.3007720Z         implementation of ``warnings.showwarning()`` and be appended to a list
2025-03-04T20:58:58.3008315Z         returned by the context manager. Otherwise None is returned by the
2025-03-04T20:58:58.3008892Z         context manager. The objects appended to the list are arguments whose
2025-03-04T20:58:58.3009432Z         attributes mirror the arguments to ``showwarning()``.
2025-03-04T20:58:58.3009852Z     modules : sequence, optional
2025-03-04T20:58:58.3010301Z         Sequence of modules for which to reset warnings registry on entry and
2025-03-04T20:58:58.3010856Z         restore on exit. To work correctly, all 'ignore' filters should
2025-03-04T20:58:58.3011303Z         filter by one of these modules.
2025-03-04T20:58:58.3011537Z 
2025-03-04T20:58:58.3011633Z     Examples
2025-03-04T20:58:58.3011877Z     --------
2025-03-04T20:58:58.3012128Z     >>> import warnings
2025-03-04T20:58:58.3012506Z     >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2025-03-04T20:58:58.3012957Z     ...         modules=[np.core.fromnumeric]):
2025-03-04T20:58:58.3013341Z     ...     warnings.simplefilter('always')
2025-03-04T20:58:58.3013818Z     ...     warnings.filterwarnings('ignore', module='np.core.fromnumeric')
2025-03-04T20:58:58.3014357Z     ...     # do something that raises a warning but ignore those in
2025-03-04T20:58:58.3014774Z     ...     # np.core.fromnumeric
2025-03-04T20:58:58.3015067Z     
2025-03-04T20:58:58.3015462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.3015854Z 
2025-03-04T20:58:58.4900493Z 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.
2025-03-04T20:58:58.4901492Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.4902109Z Applies a 1D convolution over a quantized input signal composed of
2025-03-04T20:58:58.4902577Z     several quantized input planes.
2025-03-04T20:58:58.4902837Z 
2025-03-04T20:58:58.4903072Z     For details on input arguments, parameters, and implementation see
2025-03-04T20:58:58.4903529Z     :class:`~torch.nn.Conv1d`.
2025-03-04T20:58:58.4903729Z 
2025-03-04T20:58:58.4903863Z     .. note::
2025-03-04T20:58:58.4904238Z         Only `zeros` is supported for the :attr:`padding_mode` argument.
2025-03-04T20:58:58.4904561Z 
2025-03-04T20:58:58.4904669Z     .. note::
2025-03-04T20:58:58.4904988Z         Only `torch.quint8` is supported for the input data type.
2025-03-04T20:58:58.4905469Z 
2025-03-04T20:58:58.4905474Z 
2025-03-04T20:58:58.4905584Z     Attributes:
2025-03-04T20:58:58.4905944Z         weight (Tensor):     packed tensor derived from the learnable weight
2025-03-04T20:58:58.4906403Z                              parameter.
2025-03-04T20:58:58.4906780Z         scale (Tensor):      scalar for the output scale
2025-03-04T20:58:58.4907233Z         zero_point (Tensor): scalar for the output zero point
2025-03-04T20:58:58.4907529Z 
2025-03-04T20:58:58.4907689Z     See :class:`~torch.nn.Conv1d` for other attributes.
2025-03-04T20:58:58.4907969Z 
2025-03-04T20:58:58.4908071Z     Examples::
2025-03-04T20:58:58.4908227Z 
2025-03-04T20:58:58.4908386Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2025-03-04T20:58:58.4908808Z         >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2025-03-04T20:58:58.4909194Z         >>> input = torch.randn(20, 16, 100)
2025-03-04T20:58:58.4909552Z         >>> # quantize input to quint8
2025-03-04T20:58:58.4909891Z         >>> # xdoctest: +SKIP
2025-03-04T20:58:58.4910310Z         >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2025-03-04T20:58:58.4910861Z         ...                                     dtype=torch.quint8)
2025-03-04T20:58:58.4911229Z         >>> output = m(q_input)
2025-03-04T20:58:58.4911428Z 
2025-03-04T20:58:58.4911530Z     
2025-03-04T20:58:58.4912020Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.4912401Z 
2025-03-04T20:58:58.5125824Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11.
2025-03-04T20:58:58.5126765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.5127303Z A quantized long short-term memory (LSTM).
2025-03-04T20:58:58.5127553Z 
2025-03-04T20:58:58.5127859Z     For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2025-03-04T20:58:58.5128281Z 
2025-03-04T20:58:58.5128393Z     Attributes:
2025-03-04T20:58:58.5128682Z         layers : instances of the `_LSTMLayer`
2025-03-04T20:58:58.5128934Z 
2025-03-04T20:58:58.5129054Z     .. note::
2025-03-04T20:58:58.5129435Z         To access the weights and biases, you need to access them per layer.
2025-03-04T20:58:58.5129975Z         See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2025-03-04T20:58:58.5130267Z 
2025-03-04T20:58:58.5130380Z     Examples::
2025-03-04T20:58:58.5130637Z         >>> # xdoctest: +SKIP
2025-03-04T20:58:58.5130952Z         >>> custom_module_config = {
2025-03-04T20:58:58.5131320Z         ...     'float_to_observed_custom_module_class': {
2025-03-04T20:58:58.5131721Z         ...         nn.LSTM: nn.quantizable.LSTM,
2025-03-04T20:58:58.5132064Z         ...     },
2025-03-04T20:58:58.5132365Z         ...     'observed_to_quantized_custom_module_class': {
2025-03-04T20:58:58.5132794Z         ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2025-03-04T20:58:58.5133163Z         ...     }
2025-03-04T20:58:58.5133408Z         ... }
2025-03-04T20:58:58.5133782Z         >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2025-03-04T20:58:58.5134359Z         >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2025-03-04T20:58:58.5134791Z     
2025-03-04T20:58:58.5135181Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.5135572Z 
2025-03-04T20:58:58.6247499Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227.
2025-03-04T20:58:58.6248634Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.6249216Z Squashes the sparse masks into the appropriate tensors.
2025-03-04T20:58:58.6249524Z 
2025-03-04T20:58:58.6249742Z         If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2025-03-04T20:58:58.6250280Z         the module will have a `sparse_params` dict attached to it.
2025-03-04T20:58:58.6250776Z 
2025-03-04T20:58:58.6250874Z         Args:
2025-03-04T20:58:58.6251231Z             params_to_keep: List of keys to save in the module or a dict
2025-03-04T20:58:58.6251723Z                             representing the modules and keys that will have
2025-03-04T20:58:58.6252170Z                             sparsity parameters saved
2025-03-04T20:58:58.6252649Z             params_to_keep_per_layer: Dict to specify the params that should be
2025-03-04T20:58:58.6253161Z                             saved for specific layers. The keys in the dict
2025-03-04T20:58:58.6253657Z                             should be the module fqn, while the values should
2025-03-04T20:58:58.6254122Z                             be a list of strings with the names of the variables
2025-03-04T20:58:58.6254552Z                             to save in the `sparse_params`
2025-03-04T20:58:58.6254838Z 
2025-03-04T20:58:58.6254938Z         Examples:
2025-03-04T20:58:58.6255240Z             >>> # xdoctest: +SKIP("locals are undefined")
2025-03-04T20:58:58.6255624Z             >>> # Don't save any sparse params
2025-03-04T20:58:58.6256088Z             >>> sparsifier.squash_mask()
2025-03-04T20:58:58.6256477Z             >>> hasattr(model.submodule1, 'sparse_params')
2025-03-04T20:58:58.6256843Z             False
2025-03-04T20:58:58.6256993Z 
2025-03-04T20:58:58.6257239Z             >>> # Keep sparse params per layer
2025-03-04T20:58:58.6257614Z             >>> sparsifier.squash_mask(
2025-03-04T20:58:58.6258044Z             ...     params_to_keep_per_layer={
2025-03-04T20:58:58.6258431Z             ...         'submodule1.linear1': ('foo', 'bar'),
2025-03-04T20:58:58.6258836Z             ...         'submodule2.linear42': ('baz',)
2025-03-04T20:58:58.6259175Z             ...     })
2025-03-04T20:58:58.6259515Z             >>> print(model.submodule1.linear1.sparse_params)
2025-03-04T20:58:58.6259916Z             {'foo': 42, 'bar': 24}
2025-03-04T20:58:58.6260302Z             >>> print(model.submodule2.linear42.sparse_params)
2025-03-04T20:58:58.6260695Z             {'baz': 0.1}
2025-03-04T20:58:58.6260880Z 
2025-03-04T20:58:58.6261014Z             >>> # Keep sparse params for all layers
2025-03-04T20:58:58.6261450Z             >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
2025-03-04T20:58:58.6261928Z             >>> print(model.submodule1.linear1.sparse_params)
2025-03-04T20:58:58.6262316Z             {'foo': 42, 'bar': 24}
2025-03-04T20:58:58.6262691Z             >>> print(model.submodule2.linear42.sparse_params)
2025-03-04T20:58:58.6263079Z             {'foo': 42, 'bar': 24}
2025-03-04T20:58:58.6263278Z 
2025-03-04T20:58:58.6263495Z             >>> # Keep some sparse params for all layers, and specific ones for
2025-03-04T20:58:58.6263931Z             >>> # some other layers
2025-03-04T20:58:58.6264264Z             >>> sparsifier.squash_mask(
2025-03-04T20:58:58.6264619Z             ...     params_to_keep=('foo', 'bar'),
2025-03-04T20:58:58.6264986Z             ...     params_to_keep_per_layer={
2025-03-04T20:58:58.6265365Z             ...         'submodule2.linear42': ('baz',)
2025-03-04T20:58:58.6265715Z             ...     })
2025-03-04T20:58:58.6266044Z             >>> print(model.submodule1.linear1.sparse_params)
2025-03-04T20:58:58.6266432Z             {'foo': 42, 'bar': 24}
2025-03-04T20:58:58.6266813Z             >>> print(model.submodule2.linear42.sparse_params)
2025-03-04T20:58:58.6267271Z             {'foo': 42, 'bar': 24, 'baz': 0.1}
2025-03-04T20:58:58.6267601Z         
2025-03-04T20:58:58.6267999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.6268389Z 
2025-03-04T20:58:58.7184745Z 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.
2025-03-04T20:58:58.7185820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.7186227Z 
2025-03-04T20:58:58.7186670Z Config object that specifies the supported data types passed as arguments to
2025-03-04T20:58:58.7187305Z quantize ops in the reference model spec, for input and output activations,
2025-03-04T20:58:58.7187792Z weights, and biases.
2025-03-04T20:58:58.7187973Z 
2025-03-04T20:58:58.7188142Z For example, consider the following reference model:
2025-03-04T20:58:58.7188432Z 
2025-03-04T20:58:58.7188611Z   quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2025-03-04T20:58:58.7188895Z 
2025-03-04T20:58:58.7189118Z The pattern in the square brackets refers to the reference pattern of
2025-03-04T20:58:58.7189706Z statically quantized linear. Setting the input dtype as `torch.quint8`
2025-03-04T20:58:58.7190333Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2025-03-04T20:58:58.7190926Z to the first quantize op (quant1). Similarly, setting the output dtype as
2025-03-04T20:58:58.7191513Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2025-03-04T20:58:58.7191986Z the second quantize op (quant2).
2025-03-04T20:58:58.7192206Z 
2025-03-04T20:58:58.7192435Z Note that the dtype here does not refer to the interface dtypes of the
2025-03-04T20:58:58.7193065Z op. For example, the "input dtype" here is not the dtype of the input
2025-03-04T20:58:58.7193625Z tensor passed to the quantized linear op. Though it can still be the
2025-03-04T20:58:58.7194278Z same as the interface dtype, this is not always the case, e.g. the
2025-03-04T20:58:58.7194834Z interface dtype is fp32 in dynamic quantization but the "input dtype"
2025-03-04T20:58:58.7195408Z specified in the DTypeConfig would still be quint8. The semantics of
2025-03-04T20:58:58.7195976Z dtypes here are the same as the semantics of the dtypes specified in
2025-03-04T20:58:58.7196417Z the observers.
2025-03-04T20:58:58.7196576Z 
2025-03-04T20:58:58.7196791Z These dtypes are matched against the ones specified in the user's
2025-03-04T20:58:58.7197350Z QConfig. If there is a match, and the QConfig satisfies the constraints
2025-03-04T20:58:58.7197928Z specified in the DTypeConfig (if any), then we will quantize the given
2025-03-04T20:58:58.7198512Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2025-03-04T20:58:58.7198987Z the pattern will not be quantized.
2025-03-04T20:58:58.7199199Z 
2025-03-04T20:58:58.7199339Z Example usage::
2025-03-04T20:58:58.7199486Z 
2025-03-04T20:58:58.7199615Z     >>> # xdoctest: +SKIP(failing)
2025-03-04T20:58:58.7199946Z     >>> dtype_config1 = DTypeConfig(
2025-03-04T20:58:58.7200286Z     ...     input_dtype=torch.quint8,
2025-03-04T20:58:58.7200627Z     ...     output_dtype=torch.quint8,
2025-03-04T20:58:58.7200968Z     ...     weight_dtype=torch.qint8,
2025-03-04T20:58:58.7201303Z     ...     bias_dtype=torch.float)
2025-03-04T20:58:58.7201512Z 
2025-03-04T20:58:58.7201641Z     >>> dtype_config2 = DTypeConfig(
2025-03-04T20:58:58.7201989Z     ...     input_dtype=DTypeWithConstraints(
2025-03-04T20:58:58.7202351Z     ...         dtype=torch.quint8,
2025-03-04T20:58:58.7202683Z     ...         quant_min_lower_bound=0,
2025-03-04T20:58:58.7203028Z     ...         quant_max_upper_bound=255,
2025-03-04T20:58:58.7203354Z     ...     ),
2025-03-04T20:58:58.7203637Z     ...     output_dtype=DTypeWithConstraints(
2025-03-04T20:58:58.7203999Z     ...         dtype=torch.quint8,
2025-03-04T20:58:58.7204330Z     ...         quant_min_lower_bound=0,
2025-03-04T20:58:58.7204680Z     ...         quant_max_upper_bound=255,
2025-03-04T20:58:58.7205015Z     ...     ),
2025-03-04T20:58:58.7205301Z     ...     weight_dtype=DTypeWithConstraints(
2025-03-04T20:58:58.7205661Z     ...         dtype=torch.qint8,
2025-03-04T20:58:58.7205995Z     ...         quant_min_lower_bound=-128,
2025-03-04T20:58:58.7206348Z     ...         quant_max_upper_bound=127,
2025-03-04T20:58:58.7206671Z     ...     ),
2025-03-04T20:58:58.7206929Z     ...     bias_dtype=torch.float)
2025-03-04T20:58:58.7207137Z 
2025-03-04T20:58:58.7207265Z     >>> dtype_config1.input_dtype
2025-03-04T20:58:58.7207565Z     torch.quint8
2025-03-04T20:58:58.7207783Z 
2025-03-04T20:58:58.7207899Z     >>> dtype_config2.input_dtype
2025-03-04T20:58:58.7208213Z     torch.quint8
2025-03-04T20:58:58.7208382Z 
2025-03-04T20:58:58.7208527Z     >>> dtype_config2.input_dtype_with_constraints
2025-03-04T20:58:58.7209355Z     DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2025-03-04T20:58:58.7210030Z 
2025-03-04T20:58:58.7210296Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.7210689Z 
2025-03-04T20:58:58.8471978Z 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.
2025-03-04T20:58:58.8473570Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.8474424Z 
2025-03-04T20:58:58.8474870Z Takes in optional filter values and generates two tables with desired information.
2025-03-04T20:58:58.8475305Z 
2025-03-04T20:58:58.8475720Z The generated tables are presented in both a list-of-lists format
2025-03-04T20:58:58.8476067Z 
2025-03-04T20:58:58.8476287Z The reason for the two tables are that they handle different things:
2025-03-04T20:58:58.8476920Z 1.) the first table handles all tensor level information
2025-03-04T20:58:58.8477561Z 2.) the second table handles and displays all channel based information
2025-03-04T20:58:58.8478044Z 
2025-03-04T20:58:58.8478636Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-04T20:58:58.8479617Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-04T20:58:58.8480538Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-04T20:58:58.8481244Z 
2025-03-04T20:58:58.8481357Z Tensor table columns:
2025-03-04T20:58:58.8481853Z     idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:58:58.8482348Z     ----  ---------  ---------   ---------   ---------        ---------
2025-03-04T20:58:58.8482645Z 
2025-03-04T20:58:58.8482764Z Per-Channel table columns:
2025-03-04T20:58:58.8483200Z     idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:58:58.8483726Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-04T20:58:58.8484014Z 
2025-03-04T20:58:58.8484118Z Args:
2025-03-04T20:58:58.8484521Z     feature_filter (str, optional): Filters the features presented to only those that
2025-03-04T20:58:58.8485038Z         contain this filter substring
2025-03-04T20:58:58.8485442Z         Default = "", results in all the features being printed
2025-03-04T20:58:58.8486003Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:58:58.8486651Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:58:58.8487029Z 
2025-03-04T20:58:58.8487163Z Returns a dictionary with two keys:
2025-03-04T20:58:58.8487570Z     (Dict[str, Tuple[List, List]]) A dict containing two keys:
2025-03-04T20:58:58.8488005Z     "tensor_level_info", "channel_level_info"
2025-03-04T20:58:58.8488374Z         Each key maps to a tuple with:
2025-03-04T20:58:58.8488737Z             A list of the headers of each table
2025-03-04T20:58:58.8489172Z             A list of lists containing the table information row by row
2025-03-04T20:58:58.8489654Z             The 0th index row will contain the headers of the columns
2025-03-04T20:58:58.8490087Z             The rest of the rows will contain data
2025-03-04T20:58:58.8490344Z 
2025-03-04T20:58:58.8490445Z Example Use:
2025-03-04T20:58:58.8490728Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:58.8491137Z     >>> mod_report_visualizer.generate_filtered_tables(
2025-03-04T20:58:58.8491544Z     ...     feature_filter = "per_channel_min",
2025-03-04T20:58:58.8492019Z     ...     module_fqn_filter = "block1"
2025-03-04T20:58:58.8492530Z     ... ) # generates table with per_channel_min info for all modules in block 1 of the model
2025-03-04T20:58:58.8492944Z 
2025-03-04T20:58:58.8493210Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.8493610Z 
2025-03-04T20:58:58.8494576Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400.
2025-03-04T20:58:58.8495902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.8496291Z 
2025-03-04T20:58:58.8496587Z Takes in optional filter values and prints out formatted tables of the information.
2025-03-04T20:58:58.8496987Z 
2025-03-04T20:58:58.8497353Z The reason for the two tables printed out instead of one large one are that they handle different things:
2025-03-04T20:58:58.8498086Z 1.) the first table handles all tensor level information
2025-03-04T20:58:58.8498652Z 2.) the second table handles and displays all channel based information
2025-03-04T20:58:58.8498991Z 
2025-03-04T20:58:58.8499332Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-04T20:58:58.8500201Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-04T20:58:58.8501016Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-04T20:58:58.8501506Z 
2025-03-04T20:58:58.8501625Z Tensor table columns:
2025-03-04T20:58:58.8501994Z  idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:58:58.8502475Z ----  ---------  ---------   ---------   ---------        ---------
2025-03-04T20:58:58.8502767Z 
2025-03-04T20:58:58.8502883Z Per-Channel table columns:
2025-03-04T20:58:58.8503086Z 
2025-03-04T20:58:58.8503315Z  idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:58:58.8503836Z ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-04T20:58:58.8504135Z 
2025-03-04T20:58:58.8504229Z Args:
2025-03-04T20:58:58.8504639Z     feature_filter (str, optional): Filters the features presented to only those that
2025-03-04T20:58:58.8505153Z         contain this filter substring
2025-03-04T20:58:58.8505552Z         Default = "", results in all the features being printed
2025-03-04T20:58:58.8506113Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:58:58.8506766Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:58:58.8507152Z 
2025-03-04T20:58:58.8507250Z Example Use:
2025-03-04T20:58:58.8507532Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:58.8507957Z     >>> mod_report_visualizer.generate_table_visualization(
2025-03-04T20:58:58.8508390Z     ...     feature_filter = "per_channel_min",
2025-03-04T20:58:58.8508754Z     ...     module_fqn_filter = "block1"
2025-03-04T20:58:58.8509073Z     ... )
2025-03-04T20:58:58.8509407Z     >>> # prints out neatly formatted table with per_channel_min info
2025-03-04T20:58:58.8509862Z     >>> # for all modules in block 1 of the model
2025-03-04T20:58:58.8510109Z 
2025-03-04T20:58:58.8510382Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.8510759Z 
2025-03-04T20:58:58.8511684Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566.
2025-03-04T20:58:58.8512989Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.8513376Z 
2025-03-04T20:58:58.8513637Z Takes in a feature and optional module_filter and plots of the desired data.
2025-03-04T20:58:58.8514034Z 
2025-03-04T20:58:58.8514329Z For per channel features, it averages the value across the channels and plots a point
2025-03-04T20:58:58.8515008Z per module. The reason for this is that for models with hundreds of channels, it can
2025-03-04T20:58:58.8515697Z be hard to differentiate one channel line from another, and so the point of generating
2025-03-04T20:58:58.8516382Z a single average point per module is to give a sense of general trends that encourage
2025-03-04T20:58:58.8516901Z further deep dives.
2025-03-04T20:58:58.8517060Z 
2025-03-04T20:58:58.8517164Z Note:
2025-03-04T20:58:58.8517568Z     Only features in the report that have tensor value data are plottable by this class
2025-03-04T20:58:58.8518145Z     When the tensor information is plotted, it will plot:
2025-03-04T20:58:58.8518581Z         idx as the x val, feature value as the y_val
2025-03-04T20:58:58.8519019Z     When the channel information is plotted, it will plot:
2025-03-04T20:58:58.8519574Z         the first idx of each module as the x val, feature value as the y_val [for each channel]
2025-03-04T20:58:58.8520224Z         The reason for this is that we want to be able to compare values across the
2025-03-04T20:58:58.8520869Z         channels for same layer, and it will be hard if values are staggered by idx
2025-03-04T20:58:58.8521488Z         This means each module is represented by only 1 x value
2025-03-04T20:58:58.8521884Z Args:
2025-03-04T20:58:58.8522260Z     feature_filter (str): Filters the features presented to only those that
2025-03-04T20:58:58.8522747Z         contain this filter substring
2025-03-04T20:58:58.8523250Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:58:58.8523911Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:58:58.8524305Z 
2025-03-04T20:58:58.8524408Z Example Use:
2025-03-04T20:58:58.8524697Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:58.8525127Z     >>> mod_report_visualizer.generate_plot_visualization(
2025-03-04T20:58:58.8525558Z     ...     feature_filter = "per_channel_min",
2025-03-04T20:58:58.8525932Z     ...     module_fqn_filter = "block1"
2025-03-04T20:58:58.8526257Z     ... )
2025-03-04T20:58:58.8526584Z     >>> # outputs line plot of per_channel_min information for all
2025-03-04T20:58:58.8527092Z     >>> # modules in block1 of model each channel gets it's own line,
2025-03-04T20:58:58.8527597Z     >>> # and it's plotted across the in-order modules on the x-axis
2025-03-04T20:58:58.8527895Z 
2025-03-04T20:58:58.8528168Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.8528545Z 
2025-03-04T20:58:58.8529494Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646.
2025-03-04T20:58:58.8530814Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:58.8531202Z 
2025-03-04T20:58:58.8531498Z Takes in a feature and optional module_filter and plots the histogram of desired data.
2025-03-04T20:58:58.8531909Z 
2025-03-04T20:58:58.8532009Z Note:
2025-03-04T20:58:58.8532414Z     Only features in the report that have tensor value data can be viewed as a histogram
2025-03-04T20:58:58.8533091Z     If you want to plot a histogram from all the channel values of a specific feature for
2025-03-04T20:58:58.8533743Z         a specific model, make sure to specify both the model and the feature properly
2025-03-04T20:58:58.8534376Z         in the filters and you should be able to see a distribution of the channel data
2025-03-04T20:58:58.8534749Z 
2025-03-04T20:58:58.8534851Z Args:
2025-03-04T20:58:58.8535256Z     feature_filter (str, optional): Filters the features presented to only those that
2025-03-04T20:58:58.8535774Z         contain this filter substring
2025-03-04T20:58:58.8536177Z         Default = "", results in all the features being printed
2025-03-04T20:58:58.8536782Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:58:58.8537435Z         Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:58:58.8538120Z     num_bins (int, optional): The number of bins to create the histogram with
2025-03-04T20:58:58.8538679Z         Default = 10, the values will be split into 10 equal sized bins
2025-03-04T20:58:58.8539006Z 
2025-03-04T20:58:58.8539105Z Example Use:
2025-03-04T20:58:58.8539362Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:58.8539861Z     >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2025-03-04T20:58:58.8540432Z     ...     feature_filter = "per_channel_min",
2025-03-04T20:58:58.8540801Z     ...     module_fqn_filter = "block1"
2025-03-04T20:58:58.8541120Z     ... )
2025-03-04T20:58:58.8541538Z     # outputs histogram of per_channel_min information for all modules in block1 of model
2025-03-04T20:58:58.8542219Z         information is gathered across all channels for all modules in block 1 for the
2025-03-04T20:58:58.8542882Z         per_channel_min and is displayed in a histogram of equally sized bins
2025-03-04T20:58:58.8543241Z 
2025-03-04T20:58:58.8543506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:58.8543971Z 
2025-03-04T20:58:59.1380451Z 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=666.
2025-03-04T20:58:59.1381472Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:58:59.1381866Z 
2025-03-04T20:58:59.1382150Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2025-03-04T20:58:59.1382838Z The submesh created consists of the dimensions and the communicators indicated by
2025-03-04T20:58:59.1383342Z ``mesh_dim_names``
2025-03-04T20:58:59.1383533Z 
2025-03-04T20:58:59.1383627Z Args:
2025-03-04T20:58:59.1384003Z     mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2025-03-04T20:58:59.1384578Z     mesh dimension of the DeviceMesh to create the submesh for.
2025-03-04T20:58:59.1384993Z Returns:
2025-03-04T20:58:59.1385250Z     A :class:`DeviceMesh` object
2025-03-04T20:58:59.1385455Z 
2025-03-04T20:58:59.1385777Z The following program runs on each process/rank in an SPMD manner in a world size of 8.
2025-03-04T20:58:59.1386306Z In the first example:
2025-03-04T20:58:59.1386743Z     Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2025-03-04T20:58:59.1387383Z     Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2025-03-04T20:58:59.1387997Z     Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2025-03-04T20:58:59.1388587Z     Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2025-03-04T20:58:59.1389180Z     Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2025-03-04T20:58:59.1389769Z     Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2025-03-04T20:58:59.1390115Z 
2025-03-04T20:58:59.1390235Z In the second example:
2025-03-04T20:58:59.1390700Z     Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2025-03-04T20:58:59.1391369Z     Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2025-03-04T20:58:59.1392032Z     Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2025-03-04T20:58:59.1392681Z     Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2025-03-04T20:58:59.1393078Z 
2025-03-04T20:58:59.1393213Z Example::
2025-03-04T20:58:59.1393452Z     >>> # xdoctest: +SKIP("no rank")
2025-03-04T20:58:59.1394436Z     >>> from torch.distributed.device_mesh import DeviceMesh
2025-03-04T20:58:59.1394830Z     >>>
2025-03-04T20:58:59.1395196Z     >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2025-03-04T20:58:59.1395686Z     >>> # of cross-host(dim 0), and within-host (dim 1).
2025-03-04T20:58:59.1396221Z     >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-04T20:58:59.1396721Z     >>> tp_mesh = mesh_2d["tp"]
2025-03-04T20:58:59.1397034Z     >>> dp_mesh = mesh_2d["dp"]
2025-03-04T20:58:59.1397323Z     >>>
2025-03-04T20:58:59.1397566Z     >>> # Initialize a 3D mesh.
2025-03-04T20:58:59.1398066Z     >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2025-03-04T20:58:59.1398799Z     >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2025-03-04T20:58:59.1399361Z     >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2025-03-04T20:58:59.1399713Z     >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2025-03-04T20:58:59.1399940Z 
2025-03-04T20:58:59.1400637Z Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2025-03-04T20:58:59.1401511Z 
2025-03-04T20:58:59.1401875Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-04T20:58:59.1402392Z                                                                                  ^
2025-03-04T20:58:59.1719791Z 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=2610.
2025-03-04T20:58:59.1720820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1721225Z 
2025-03-04T20:58:59.1721493Z Send or Receive a batch of tensors asynchronously and return a list of requests.
2025-03-04T20:58:59.1721884Z 
2025-03-04T20:58:59.1722142Z Process each of the operations in ``p2p_op_list`` and return the corresponding
2025-03-04T20:58:59.1722758Z requests. NCCL, Gloo, and UCC backend are currently supported.
2025-03-04T20:58:59.1723095Z 
2025-03-04T20:58:59.1723188Z Args:
2025-03-04T20:58:59.1723562Z     p2p_op_list: A list of point-to-point operations(type of each operator is
2025-03-04T20:58:59.1724168Z         ``torch.distributed.P2POp``). The order of the isend/irecv in the list
2025-03-04T20:58:59.1724750Z         matters and it needs to match with corresponding isend/irecv on the
2025-03-04T20:58:59.1725195Z         remote end.
2025-03-04T20:58:59.1725349Z 
2025-03-04T20:58:59.1725459Z Returns:
2025-03-04T20:58:59.1725853Z     A list of distributed request objects returned by calling the corresponding
2025-03-04T20:58:59.1726512Z     op in the op_list.
2025-03-04T20:58:59.1726706Z 
2025-03-04T20:58:59.1726817Z Examples:
2025-03-04T20:58:59.1727137Z     >>> # xdoctest: +SKIP("no rank")
2025-03-04T20:58:59.1727566Z     >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
2025-03-04T20:58:59.1728104Z     >>> recv_tensor = torch.randn(2, dtype=torch.float32)
2025-03-04T20:58:59.1728668Z     >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
2025-03-04T20:58:59.1729114Z     >>> recv_op = dist.P2POp(
2025-03-04T20:58:59.1729569Z     ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
2025-03-04T20:58:59.1729992Z     ... )
2025-03-04T20:58:59.1730322Z     >>> reqs = batch_isend_irecv([send_op, recv_op])
2025-03-04T20:58:59.1730687Z     >>> for req in reqs:
2025-03-04T20:58:59.1731018Z     >>>     req.wait()
2025-03-04T20:58:59.1731284Z     >>> recv_tensor
2025-03-04T20:58:59.1731551Z     tensor([2, 3])     # Rank 0
2025-03-04T20:58:59.1731892Z     tensor([0, 1])     # Rank 1
2025-03-04T20:58:59.1732079Z 
2025-03-04T20:58:59.1732445Z .. note:: Note that when this API is used with the NCCL PG backend, users must set
2025-03-04T20:58:59.1733054Z     the current GPU device with `torch.cuda.set_device`, otherwise it will
2025-03-04T20:58:59.1733769Z     lead to unexpected hang issues.
2025-03-04T20:58:59.1734056Z 
2025-03-04T20:58:59.1734276Z     In addition, if this API is the first collective call in the ``group``
2025-03-04T20:58:59.1734906Z     passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
2025-03-04T20:58:59.1735556Z     this API call; otherwise, the behavior is undefined. If this API call is
2025-03-04T20:58:59.1736170Z     not the first collective call in the ``group``, batched P2P operations
2025-03-04T20:58:59.1736759Z     involving only a subset of ranks of the ``group`` are allowed.
2025-03-04T20:58:59.1737144Z 
2025-03-04T20:58:59.1737405Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.1737923Z 
2025-03-04T20:58:59.1738559Z 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=2735.
2025-03-04T20:58:59.1739586Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1740020Z 
2025-03-04T20:58:59.1740314Z Reduces the tensor data across all machines in a way that all get the final result.
2025-03-04T20:58:59.1740870Z 
2025-03-04T20:58:59.1741110Z After the call ``tensor`` is going to be bitwise identical in all processes.
2025-03-04T20:58:59.1741483Z 
2025-03-04T20:58:59.1741769Z Complex tensors are supported.
2025-03-04T20:58:59.1741989Z 
2025-03-04T20:58:59.1742082Z Args:
2025-03-04T20:58:59.1742489Z     tensor (Tensor): Input and output of the collective. The function
2025-03-04T20:58:59.1742939Z         operates in-place.
2025-03-04T20:58:59.1743316Z     op (optional): One of the values from
2025-03-04T20:58:59.1743803Z         ``torch.distributed.ReduceOp``
2025-03-04T20:58:59.1744340Z         enum.  Specifies an operation used for element-wise reductions.
2025-03-04T20:58:59.1745019Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:58:59.1745585Z         the default process group will be used.
2025-03-04T20:58:59.1746052Z     async_op (bool, optional): Whether this op should be an async op
2025-03-04T20:58:59.1746432Z 
2025-03-04T20:58:59.1746539Z Returns:
2025-03-04T20:58:59.1746820Z     Async work handle, if async_op is set to True.
2025-03-04T20:58:59.1747300Z     None, if not async_op or if not part of the group
2025-03-04T20:58:59.1747571Z 
2025-03-04T20:58:59.1747681Z Examples:
2025-03-04T20:58:59.1747977Z     >>> # xdoctest: +SKIP("no rank")
2025-03-04T20:58:59.1748347Z     >>> # All tensors below are of torch.int64 type.
2025-03-04T20:58:59.1748795Z     >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:58:59.1749164Z     >>> device = torch.device(f"cuda:{rank}")
2025-03-04T20:58:59.1749706Z     >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-04T20:58:59.1750212Z     >>> tensor
2025-03-04T20:58:59.1750481Z     tensor([1, 2], device='cuda:0') # Rank 0
2025-03-04T20:58:59.1750897Z     tensor([3, 4], device='cuda:1') # Rank 1
2025-03-04T20:58:59.1751279Z     >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-04T20:58:59.1751676Z     >>> tensor
2025-03-04T20:58:59.1751946Z     tensor([4, 6], device='cuda:0') # Rank 0
2025-03-04T20:58:59.1752355Z     tensor([4, 6], device='cuda:1') # Rank 1
2025-03-04T20:58:59.1752616Z 
2025-03-04T20:58:59.1752780Z     >>> # All tensors below are of torch.cfloat type.
2025-03-04T20:58:59.1753219Z     >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:58:59.1753567Z     >>> tensor = torch.tensor(
2025-03-04T20:58:59.1753968Z     ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-04T20:58:59.1754348Z     ... ) + 2 * rank * (1 + 1j)
2025-03-04T20:58:59.1754658Z     >>> tensor
2025-03-04T20:58:59.1754974Z     tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-04T20:58:59.1755398Z     tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-04T20:58:59.1755843Z     >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-04T20:58:59.1756195Z     >>> tensor
2025-03-04T20:58:59.1756619Z     tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
2025-03-04T20:58:59.1757027Z     tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
2025-03-04T20:58:59.1757300Z 
2025-03-04T20:58:59.1757305Z 
2025-03-04T20:58:59.1757570Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.1758024Z 
2025-03-04T20:58:59.1758682Z 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=3079.
2025-03-04T20:58:59.1759676Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1760140Z 
2025-03-04T20:58:59.1760374Z Gathers picklable objects from the whole group in a single process.
2025-03-04T20:58:59.1760733Z 
2025-03-04T20:58:59.1760977Z Similar to :func:`gather`, but Python objects can be passed in. Note that the
2025-03-04T20:58:59.1761500Z object must be picklable in order to be gathered.
2025-03-04T20:58:59.1761784Z 
2025-03-04T20:58:59.1761879Z Args:
2025-03-04T20:58:59.1762150Z     obj (Any): Input object. Must be picklable.
2025-03-04T20:58:59.1762678Z     object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2025-03-04T20:58:59.1763219Z         should be correctly sized as the size of the group for this
2025-03-04T20:58:59.1763903Z         collective and will contain the output. Must be ``None`` on non-dst
2025-03-04T20:58:59.1764371Z         ranks. (default is ``None``)
2025-03-04T20:58:59.1764924Z     dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
2025-03-04T20:58:59.1765586Z         (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
2025-03-04T20:58:59.1766158Z     group: (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:58:59.1766721Z         the default process group will be used. Default is ``None``.
2025-03-04T20:58:59.1767395Z     group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
2025-03-04T20:58:59.1767864Z 
2025-03-04T20:58:59.1767978Z Returns:
2025-03-04T20:58:59.1768310Z     None. On the ``dst`` rank, ``object_gather_list`` will contain the
2025-03-04T20:58:59.1768733Z     output of the collective.
2025-03-04T20:58:59.1768931Z 
2025-03-04T20:58:59.1769178Z .. note:: Note that this API differs slightly from the gather collective
2025-03-04T20:58:59.1769743Z     since it does not provide an async_op handle and thus will be a blocking
2025-03-04T20:58:59.1770184Z     call.
2025-03-04T20:58:59.1770311Z 
2025-03-04T20:58:59.1770564Z .. note:: For NCCL-based processed groups, internal tensor representations
2025-03-04T20:58:59.1771201Z     of objects must be moved to the GPU device before communication takes
2025-03-04T20:58:59.1771698Z     place. In this case, the device used is given by
2025-03-04T20:58:59.1772195Z     ``torch.cuda.current_device()`` and it is the user's responsiblity to
2025-03-04T20:58:59.1772759Z     ensure that this is set so that each rank has an individual GPU, via
2025-03-04T20:58:59.1773217Z     ``torch.cuda.set_device()``.
2025-03-04T20:58:59.1773421Z 
2025-03-04T20:58:59.1773522Z .. warning::
2025-03-04T20:58:59.1774093Z     :func:`gather_object` uses ``pickle`` module implicitly, which is
2025-03-04T20:58:59.1774650Z     known to be insecure. It is possible to construct malicious pickle data
2025-03-04T20:58:59.1775234Z     which will execute arbitrary code during unpickling. Only call this
2025-03-04T20:58:59.1775704Z     function with data you trust.
2025-03-04T20:58:59.1775929Z 
2025-03-04T20:58:59.1776029Z .. warning::
2025-03-04T20:58:59.1776400Z     Calling :func:`gather_object` with GPU tensors is not well supported
2025-03-04T20:58:59.1776982Z     and inefficient as it incurs GPU -> CPU transfer since tensors would be
2025-03-04T20:58:59.1777519Z     pickled. Please consider using :func:`gather` instead.
2025-03-04T20:58:59.1777885Z 
2025-03-04T20:58:59.1777985Z Example::
2025-03-04T20:58:59.1778270Z     >>> # xdoctest: +SKIP("need process group init")
2025-03-04T20:58:59.1778818Z     >>> # Note: Process group initialization omitted on each rank.
2025-03-04T20:58:59.1779253Z     >>> import torch.distributed as dist
2025-03-04T20:58:59.1779607Z     >>> # Assumes world_size of 3.
2025-03-04T20:58:59.1780013Z     >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2025-03-04T20:58:59.1780460Z     >>> output = [None for _ in gather_objects]
2025-03-04T20:58:59.1780822Z     >>> dist.gather_object(
2025-03-04T20:58:59.1781147Z     ...     gather_objects[dist.get_rank()],
2025-03-04T20:58:59.1781532Z     ...     output if dist.get_rank() == 0 else None,
2025-03-04T20:58:59.1781882Z     ...     dst=0
2025-03-04T20:58:59.1782128Z     ... )
2025-03-04T20:58:59.1782345Z     >>> # On rank 0
2025-03-04T20:58:59.1782594Z     >>> output
2025-03-04T20:58:59.1782838Z     ['foo', 12, {1: 2}]
2025-03-04T20:58:59.1783010Z 
2025-03-04T20:58:59.1783271Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.1783708Z 
2025-03-04T20:58:59.1786190Z 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=3655.
2025-03-04T20:58:59.1787303Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1787707Z 
2025-03-04T20:58:59.1787945Z Gathers tensors from the whole group in a list.
2025-03-04T20:58:59.1788224Z 
2025-03-04T20:58:59.1788378Z Complex and uneven sized tensors are supported.
2025-03-04T20:58:59.1788656Z 
2025-03-04T20:58:59.1788747Z Args:
2025-03-04T20:58:59.1789068Z     tensor_list (list[Tensor]): Output list. It should contain
2025-03-04T20:58:59.1789605Z         correctly-sized tensors to be used for output of the collective.
2025-03-04T20:58:59.1790079Z         Uneven sized tensors are supported.
2025-03-04T20:58:59.1790520Z     tensor (Tensor): Tensor to be broadcast from current process.
2025-03-04T20:58:59.1791080Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:58:59.1791597Z         the default process group will be used.
2025-03-04T20:58:59.1792057Z     async_op (bool, optional): Whether this op should be an async op
2025-03-04T20:58:59.1792384Z 
2025-03-04T20:58:59.1792478Z Returns:
2025-03-04T20:58:59.1792756Z     Async work handle, if async_op is set to True.
2025-03-04T20:58:59.1793180Z     None, if not async_op or if not part of the group
2025-03-04T20:58:59.1793456Z 
2025-03-04T20:58:59.1793551Z Examples:
2025-03-04T20:58:59.1793833Z     >>> # xdoctest: +SKIP("need process group init")
2025-03-04T20:58:59.1794239Z     >>> # All tensors below are of torch.int64 dtype.
2025-03-04T20:58:59.1794624Z     >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:58:59.1794993Z     >>> device = torch.device(f"cuda:{rank}")
2025-03-04T20:58:59.1795343Z     >>> tensor_list = [
2025-03-04T20:58:59.1795741Z     ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
2025-03-04T20:58:59.1796189Z     ... ]
2025-03-04T20:58:59.1796420Z     >>> tensor_list
2025-03-04T20:58:59.1796793Z     [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
2025-03-04T20:58:59.1797335Z     [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
2025-03-04T20:58:59.1797888Z     >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-04T20:58:59.1798347Z     >>> tensor
2025-03-04T20:58:59.1798618Z     tensor([1, 2], device='cuda:0') # Rank 0
2025-03-04T20:58:59.1798979Z     tensor([3, 4], device='cuda:1') # Rank 1
2025-03-04T20:58:59.1799339Z     >>> dist.all_gather(tensor_list, tensor)
2025-03-04T20:58:59.1799674Z     >>> tensor_list
2025-03-04T20:58:59.1800034Z     [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
2025-03-04T20:58:59.1800565Z     [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
2025-03-04T20:58:59.1800898Z 
2025-03-04T20:58:59.1801049Z     >>> # All tensors below are of torch.cfloat dtype.
2025-03-04T20:58:59.1801487Z     >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:58:59.1801834Z     >>> tensor_list = [
2025-03-04T20:58:59.1802238Z     ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
2025-03-04T20:58:59.1802684Z     ... ]
2025-03-04T20:58:59.1802924Z     >>> tensor_list
2025-03-04T20:58:59.1803352Z     [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
2025-03-04T20:58:59.1803990Z     [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
2025-03-04T20:58:59.1804485Z     >>> tensor = torch.tensor(
2025-03-04T20:58:59.1804849Z     ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-04T20:58:59.1805237Z     ... ) + 2 * rank * (1 + 1j)
2025-03-04T20:58:59.1805531Z     >>> tensor
2025-03-04T20:58:59.1805816Z     tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-04T20:58:59.1806226Z     tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-04T20:58:59.1806621Z     >>> dist.all_gather(tensor_list, tensor)
2025-03-04T20:58:59.1806958Z     >>> tensor_list
2025-03-04T20:58:59.1807398Z     [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
2025-03-04T20:58:59.1808025Z     [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
2025-03-04T20:58:59.1808466Z 
2025-03-04T20:58:59.1808471Z 
2025-03-04T20:58:59.1808738Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.1809125Z 
2025-03-04T20:58:59.1845467Z 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=4343.
2025-03-04T20:58:59.1847449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1848189Z 
2025-03-04T20:58:59.1848699Z Split input tensor and then scatter the split list to all processes in a group.
2025-03-04T20:58:59.1849467Z 
2025-03-04T20:58:59.1849989Z Later the received tensors are concatenated from all the processes in the group
2025-03-04T20:58:59.1850932Z and returned as a single output tensor.
2025-03-04T20:58:59.1851342Z 
2025-03-04T20:58:59.1851562Z Complex tensors are supported.
2025-03-04T20:58:59.1851942Z 
2025-03-04T20:58:59.1852118Z Args:
2025-03-04T20:58:59.1852633Z     output (Tensor): Gathered concatenated output tensor.
2025-03-04T20:58:59.1853406Z     input (Tensor): Input tensor to scatter.
2025-03-04T20:58:59.1854276Z     output_split_sizes: (list[Int], optional): Output split sizes for dim 0
2025-03-04T20:58:59.1855336Z         if specified None or empty, dim 0 of ``output`` tensor must divide
2025-03-04T20:58:59.1856159Z         equally by ``world_size``.
2025-03-04T20:58:59.1856956Z     input_split_sizes: (list[Int], optional): Input split sizes for dim 0
2025-03-04T20:58:59.1858050Z         if specified None or empty, dim 0 of ``input`` tensor must divide
2025-03-04T20:58:59.1858870Z         equally by ``world_size``.
2025-03-04T20:58:59.1859695Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:58:59.1860624Z         the default process group will be used.
2025-03-04T20:58:59.1861464Z     async_op (bool, optional): Whether this op should be an async op.
2025-03-04T20:58:59.1862095Z 
2025-03-04T20:58:59.1862263Z Returns:
2025-03-04T20:58:59.1862777Z     Async work handle, if async_op is set to True.
2025-03-04T20:58:59.1863535Z     None, if not async_op or if not part of the group.
2025-03-04T20:58:59.1864022Z 
2025-03-04T20:58:59.1864197Z .. warning::
2025-03-04T20:58:59.1864711Z     `all_to_all_single` is experimental and subject to change.
2025-03-04T20:58:59.1865195Z 
2025-03-04T20:58:59.1865357Z Examples:
2025-03-04T20:58:59.1865779Z     >>> # xdoctest: +SKIP("Undefined rank")
2025-03-04T20:58:59.1866416Z     >>> input = torch.arange(4) + rank * 4
2025-03-04T20:58:59.1867017Z     >>> input
2025-03-04T20:58:59.1867464Z     tensor([0, 1, 2, 3])     # Rank 0
2025-03-04T20:58:59.1868208Z     tensor([4, 5, 6, 7])     # Rank 1
2025-03-04T20:58:59.1868782Z     tensor([8, 9, 10, 11])   # Rank 2
2025-03-04T20:58:59.1869366Z     tensor([12, 13, 14, 15]) # Rank 3
2025-03-04T20:58:59.1870024Z     >>> output = torch.empty([4], dtype=torch.int64)
2025-03-04T20:58:59.1870736Z     >>> dist.all_to_all_single(output, input)
2025-03-04T20:58:59.1871363Z     >>> output
2025-03-04T20:58:59.1871812Z     tensor([0, 4, 8, 12])    # Rank 0
2025-03-04T20:58:59.1872354Z     tensor([1, 5, 9, 13])    # Rank 1
2025-03-04T20:58:59.1872909Z     tensor([2, 6, 10, 14])   # Rank 2
2025-03-04T20:58:59.1873462Z     tensor([3, 7, 11, 15])   # Rank 3
2025-03-04T20:58:59.1874042Z 
2025-03-04T20:58:59.1874353Z     >>> # Essentially, it is similar to following operation:
2025-03-04T20:58:59.1875156Z     >>> scatter_list = list(input.chunk(world_size))
2025-03-04T20:58:59.1875893Z     >>> gather_list = list(output.chunk(world_size))
2025-03-04T20:58:59.1876581Z     >>> for i in range(world_size):
2025-03-04T20:58:59.1877371Z     >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
2025-03-04T20:58:59.1878196Z 
2025-03-04T20:58:59.1878411Z     >>> # Another example with uneven split
2025-03-04T20:58:59.1878993Z     >>> input
2025-03-04T20:58:59.1879533Z     tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-04T20:58:59.1880527Z     tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-04T20:58:59.1881347Z     tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-04T20:58:59.1882199Z     tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-04T20:58:59.1882921Z     >>> input_splits
2025-03-04T20:58:59.1883444Z     [2, 2, 1, 1]                                                     # Rank 0
2025-03-04T20:58:59.1884153Z     [3, 2, 2, 2]                                                     # Rank 1
2025-03-04T20:58:59.1884863Z     [2, 1, 1, 1]                                                     # Rank 2
2025-03-04T20:58:59.1885568Z     [2, 2, 2, 1]                                                     # Rank 3
2025-03-04T20:58:59.1886221Z     >>> output_splits
2025-03-04T20:58:59.1886763Z     [2, 3, 2, 2]                                                     # Rank 0
2025-03-04T20:58:59.1887493Z     [2, 2, 1, 2]                                                     # Rank 1
2025-03-04T20:58:59.1888205Z     [1, 2, 1, 2]                                                     # Rank 2
2025-03-04T20:58:59.1888906Z     [1, 2, 1, 1]                                                     # Rank 3
2025-03-04T20:58:59.1889558Z     >>> output = ...
2025-03-04T20:58:59.1890253Z     >>> dist.all_to_all_single(output, input, output_splits, input_splits)
2025-03-04T20:58:59.1891060Z     >>> output
2025-03-04T20:58:59.1891629Z     tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
2025-03-04T20:58:59.1892466Z     tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
2025-03-04T20:58:59.1893329Z     tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
2025-03-04T20:58:59.1894149Z     tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
2025-03-04T20:58:59.1894668Z 
2025-03-04T20:58:59.1894692Z 
2025-03-04T20:58:59.1894996Z     >>> # Another example with tensors of torch.cfloat type.
2025-03-04T20:58:59.1895755Z     >>> input = torch.tensor(
2025-03-04T20:58:59.1896392Z     ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-04T20:58:59.1897062Z     ... ) + 4 * rank * (1 + 1j)
2025-03-04T20:58:59.1897579Z     >>> input
2025-03-04T20:58:59.1898247Z     tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
2025-03-04T20:58:59.1899161Z     tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
2025-03-04T20:58:59.1900023Z     tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
2025-03-04T20:58:59.1900911Z     tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
2025-03-04T20:58:59.1901928Z     >>> output = torch.empty([4], dtype=torch.int64)
2025-03-04T20:58:59.1902661Z     >>> dist.all_to_all_single(output, input)
2025-03-04T20:58:59.1903252Z     >>> output
2025-03-04T20:58:59.1903845Z     tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
2025-03-04T20:58:59.1904700Z     tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
2025-03-04T20:58:59.1905593Z     tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
2025-03-04T20:58:59.1906469Z     tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
2025-03-04T20:58:59.1907041Z 
2025-03-04T20:58:59.1907535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.1908241Z 
2025-03-04T20:58:59.1909364Z 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=4481.
2025-03-04T20:58:59.1911235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1911974Z 
2025-03-04T20:58:59.1912812Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
2025-03-04T20:58:59.1913711Z 
2025-03-04T20:58:59.1913923Z Complex tensors are supported.
2025-03-04T20:58:59.1914283Z 
2025-03-04T20:58:59.1914566Z Args:
2025-03-04T20:58:59.1915218Z     output_tensor_list (list[Tensor]): List of tensors to be gathered one
2025-03-04T20:58:59.1916035Z         per rank.
2025-03-04T20:58:59.1916742Z     input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
2025-03-04T20:58:59.1917821Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:58:59.1918722Z         the default process group will be used.
2025-03-04T20:58:59.1919557Z     async_op (bool, optional): Whether this op should be an async op.
2025-03-04T20:58:59.1920147Z 
2025-03-04T20:58:59.1920313Z Returns:
2025-03-04T20:58:59.1920821Z     Async work handle, if async_op is set to True.
2025-03-04T20:58:59.1921548Z     None, if not async_op or if not part of the group.
2025-03-04T20:58:59.1922027Z 
2025-03-04T20:58:59.1922172Z .. warning::
2025-03-04T20:58:59.1922606Z     `all_to_all` is experimental and subject to change.
2025-03-04T20:58:59.1923091Z 
2025-03-04T20:58:59.1923242Z Examples:
2025-03-04T20:58:59.1923639Z     >>> # xdoctest: +SKIP("Undefined rank")
2025-03-04T20:58:59.1924124Z     >>> input = torch.arange(4) + rank * 4
2025-03-04T20:58:59.1924480Z     >>> input = list(input.chunk(4))
2025-03-04T20:58:59.1924801Z     >>> input
2025-03-04T20:58:59.1925129Z     [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
2025-03-04T20:58:59.1925609Z     [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
2025-03-04T20:58:59.1926081Z     [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
2025-03-04T20:58:59.1926554Z     [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
2025-03-04T20:58:59.1927048Z     >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-04T20:58:59.1927484Z     >>> dist.all_to_all(output, input)
2025-03-04T20:58:59.1927812Z     >>> output
2025-03-04T20:58:59.1928124Z     [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
2025-03-04T20:58:59.1928602Z     [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
2025-03-04T20:58:59.1929070Z     [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
2025-03-04T20:58:59.1929575Z     [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
2025-03-04T20:58:59.1930066Z 
2025-03-04T20:58:59.1930363Z     >>> # Essentially, it is similar to following operation:
2025-03-04T20:58:59.1931032Z     >>> scatter_list = input
2025-03-04T20:58:59.1931529Z     >>> gather_list = output
2025-03-04T20:58:59.1932037Z     >>> for i in range(world_size):
2025-03-04T20:58:59.1932806Z     >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
2025-03-04T20:58:59.1933488Z 
2025-03-04T20:58:59.1933639Z     >>> input
2025-03-04T20:58:59.1934129Z     tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-04T20:58:59.1934856Z     tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-04T20:58:59.1935619Z     tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-04T20:58:59.1936326Z     tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-04T20:58:59.1936918Z     >>> input_splits
2025-03-04T20:58:59.1937358Z     [2, 2, 1, 1]                                                     # Rank 0
2025-03-04T20:58:59.1938054Z     [3, 2, 2, 2]                                                     # Rank 1
2025-03-04T20:58:59.1938658Z     [2, 1, 1, 1]                                                     # Rank 2
2025-03-04T20:58:59.1939253Z     [2, 2, 2, 1]                                                     # Rank 3
2025-03-04T20:58:59.1939790Z     >>> output_splits
2025-03-04T20:58:59.1940244Z     [2, 3, 2, 2]                                                     # Rank 0
2025-03-04T20:58:59.1940957Z     [2, 2, 1, 2]                                                     # Rank 1
2025-03-04T20:58:59.1941669Z     [1, 2, 1, 2]                                                     # Rank 2
2025-03-04T20:58:59.1942474Z     [1, 2, 1, 1]                                                     # Rank 3
2025-03-04T20:58:59.1943189Z     >>> input = list(input.split(input_splits))
2025-03-04T20:58:59.1943811Z     >>> input
2025-03-04T20:58:59.1944411Z     [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
2025-03-04T20:58:59.1945429Z     [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
2025-03-04T20:58:59.1946420Z     [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
2025-03-04T20:58:59.1947463Z     [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
2025-03-04T20:58:59.1948266Z     >>> output = ...
2025-03-04T20:58:59.1948782Z     >>> dist.all_to_all(output, input)
2025-03-04T20:58:59.1949382Z     >>> output
2025-03-04T20:58:59.1949985Z     [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
2025-03-04T20:58:59.1950999Z     [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
2025-03-04T20:58:59.1952018Z     [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
2025-03-04T20:58:59.1952918Z     [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
2025-03-04T20:58:59.1953489Z 
2025-03-04T20:58:59.1953744Z     >>> # Another example with tensors of torch.cfloat type.
2025-03-04T20:58:59.1954369Z     >>> input = torch.tensor(
2025-03-04T20:58:59.1954917Z     ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-04T20:58:59.1955573Z     ... ) + 4 * rank * (1 + 1j)
2025-03-04T20:58:59.1956068Z     >>> input = list(input.chunk(4))
2025-03-04T20:58:59.1956477Z     >>> input
2025-03-04T20:58:59.1956852Z     [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
2025-03-04T20:58:59.1957431Z     [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
2025-03-04T20:58:59.1958014Z     [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
2025-03-04T20:58:59.1958600Z     [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
2025-03-04T20:58:59.1959152Z     >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-04T20:58:59.1959593Z     >>> dist.all_to_all(output, input)
2025-03-04T20:58:59.1959924Z     >>> output
2025-03-04T20:58:59.1960302Z     [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
2025-03-04T20:58:59.1960857Z     [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
2025-03-04T20:58:59.1961429Z     [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
2025-03-04T20:58:59.1962437Z     [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
2025-03-04T20:58:59.1963042Z 
2025-03-04T20:58:59.1963049Z 
2025-03-04T20:58:59.1963504Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.1964195Z 
2025-03-04T20:58:59.1964985Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2.
2025-03-04T20:58:59.1965871Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.1984844Z 
2025-03-04T20:58:59.1985153Z Module ``torch.distributed.launch``.
2025-03-04T20:58:59.1985610Z 
2025-03-04T20:58:59.1986078Z ``torch.distributed.launch`` is a module that spawns up multiple distributed
2025-03-04T20:58:59.1987072Z training processes on each of the training nodes.
2025-03-04T20:58:59.1987585Z 
2025-03-04T20:58:59.1987758Z .. warning::
2025-03-04T20:58:59.1988008Z 
2025-03-04T20:58:59.1988480Z     This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2025-03-04T20:58:59.1989189Z 
2025-03-04T20:58:59.1989447Z The utility can be used for single-node distributed training, in which one or
2025-03-04T20:58:59.1990187Z more processes per node will be spawned. The utility can be used for either
2025-03-04T20:58:59.1990793Z CPU training or GPU training. If the utility is used for GPU training,
2025-03-04T20:58:59.1991402Z each distributed process will be operating on a single GPU. This can achieve
2025-03-04T20:58:59.1992031Z well-improved single-node training performance. It can also be used in
2025-03-04T20:58:59.1992677Z multi-node distributed training, by spawning up multiple processes on each node
2025-03-04T20:58:59.1993320Z for well-improved multi-node distributed training performance as well.
2025-03-04T20:58:59.1993921Z This will especially be beneficial for systems with multiple Infiniband
2025-03-04T20:58:59.1994553Z interfaces that have direct-GPU support, since all of them can be utilized for
2025-03-04T20:58:59.1995069Z aggregated communication bandwidth.
2025-03-04T20:58:59.1995300Z 
2025-03-04T20:58:59.1995715Z In both cases of single-node distributed training or multi-node distributed
2025-03-04T20:58:59.1996764Z training, this utility will launch the given number of processes per node
2025-03-04T20:58:59.1997842Z (``--nproc-per-node``). If used for GPU training, this number needs to be less
2025-03-04T20:58:59.1998898Z or equal to the number of GPUs on the current system (``nproc_per_node``),
2025-03-04T20:58:59.1999925Z and each process will be operating on a single GPU from *GPU 0 to
2025-03-04T20:58:59.2000683Z GPU (nproc_per_node - 1)*.
2025-03-04T20:58:59.2001015Z 
2025-03-04T20:58:59.2001220Z **How to use this module:**
2025-03-04T20:58:59.2001547Z 
2025-03-04T20:58:59.2001844Z 1. Single-Node multi-process distributed training
2025-03-04T20:58:59.2002321Z 
2025-03-04T20:58:59.2002509Z ::
2025-03-04T20:58:59.2002706Z 
2025-03-04T20:58:59.2003153Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-04T20:58:59.2004180Z                YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2025-03-04T20:58:59.2004985Z                arguments of your training script)
2025-03-04T20:58:59.2005307Z 
2025-03-04T20:58:59.2005732Z 2. Multi-Node multi-process distributed training: (e.g. two nodes)
2025-03-04T20:58:59.2006350Z 
2025-03-04T20:58:59.2006357Z 
2025-03-04T20:58:59.2006651Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2025-03-04T20:58:59.2007132Z 
2025-03-04T20:58:59.2007304Z ::
2025-03-04T20:58:59.2007500Z 
2025-03-04T20:58:59.2007960Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-04T20:58:59.2008945Z                --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2025-03-04T20:58:59.2009811Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-04T20:58:59.2010840Z                and all other arguments of your training script)
2025-03-04T20:58:59.2011357Z 
2025-03-04T20:58:59.2011515Z Node 2:
2025-03-04T20:58:59.2011753Z 
2025-03-04T20:58:59.2011915Z ::
2025-03-04T20:58:59.2012132Z 
2025-03-04T20:58:59.2012572Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-04T20:58:59.2013519Z                --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2025-03-04T20:58:59.2014424Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-04T20:58:59.2015330Z                and all other arguments of your training script)
2025-03-04T20:58:59.2015818Z 
2025-03-04T20:58:59.2016131Z 3. To look up what optional arguments this module offers:
2025-03-04T20:58:59.2016626Z 
2025-03-04T20:58:59.2016780Z ::
2025-03-04T20:58:59.2016998Z 
2025-03-04T20:58:59.2017253Z     python -m torch.distributed.launch --help
2025-03-04T20:58:59.2017705Z 
2025-03-04T20:58:59.2017712Z 
2025-03-04T20:58:59.2017981Z **Important Notices:**
2025-03-04T20:58:59.2018317Z 
2025-03-04T20:58:59.2018672Z 1. This utility and multi-process distributed (single-node or
2025-03-04T20:58:59.2019770Z multi-node) GPU training currently only achieves the best performance using
2025-03-04T20:58:59.2020910Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2025-03-04T20:58:59.2021804Z use for GPU training.
2025-03-04T20:58:59.2022244Z 
2025-03-04T20:58:59.2022650Z 2. In your training program, you must parse the command-line argument:
2025-03-04T20:58:59.2023703Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2025-03-04T20:58:59.2024814Z If your training program uses GPUs, you should ensure that your code only
2025-03-04T20:58:59.2025835Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2025-03-04T20:58:59.2026446Z 
2025-03-04T20:58:59.2026651Z Parsing the local_rank argument
2025-03-04T20:58:59.2027021Z 
2025-03-04T20:58:59.2027210Z ::
2025-03-04T20:58:59.2027418Z 
2025-03-04T20:58:59.2027627Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.2028136Z     >>> import argparse
2025-03-04T20:58:59.2028687Z     >>> parser = argparse.ArgumentParser()
2025-03-04T20:58:59.2029392Z     >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2025-03-04T20:58:59.2030158Z     >>> args = parser.parse_args()
2025-03-04T20:58:59.2030504Z 
2025-03-04T20:58:59.2030664Z Set your device to local rank using either
2025-03-04T20:58:59.2030903Z 
2025-03-04T20:58:59.2031017Z ::
2025-03-04T20:58:59.2031134Z 
2025-03-04T20:58:59.2031358Z     >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2025-03-04T20:58:59.2031686Z 
2025-03-04T20:58:59.2031789Z or
2025-03-04T20:58:59.2031907Z 
2025-03-04T20:58:59.2032011Z ::
2025-03-04T20:58:59.2032124Z 
2025-03-04T20:58:59.2032278Z     >>> with torch.cuda.device(args.local_rank):
2025-03-04T20:58:59.2032629Z     >>>    # your code to run
2025-03-04T20:58:59.2032919Z     >>>    ...
2025-03-04T20:58:59.2033070Z 
2025-03-04T20:58:59.2033180Z .. versionchanged:: 2.0.0
2025-03-04T20:58:59.2033379Z 
2025-03-04T20:58:59.2033635Z     The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2025-03-04T20:58:59.2034268Z     From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2025-03-04T20:58:59.2034794Z     previously used underscored ``--local_rank``.
2025-03-04T20:58:59.2035068Z 
2025-03-04T20:58:59.2035317Z     For backward compatibility, it may be necessary for users to handle both
2025-03-04T20:58:59.2035967Z     cases in their argument parsing code. This means including both ``"--local-rank"``
2025-03-04T20:58:59.2036587Z     and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2025-03-04T20:58:59.2037204Z     provided, the launcher will trigger an error: "error: unrecognized arguments:
2025-03-04T20:58:59.2038221Z     --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2025-03-04T20:58:59.2039132Z     including ``"--local-rank"`` should be sufficient.
2025-03-04T20:58:59.2039758Z 
2025-03-04T20:58:59.2040179Z 3. In your training program, you are supposed to call the following function
2025-03-04T20:58:59.2041229Z at the beginning to start the distributed backend. It is strongly recommended
2025-03-04T20:58:59.2042352Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2025-03-04T20:58:59.2043356Z but ``env://`` is the one that is officially supported by this module.
2025-03-04T20:58:59.2043966Z 
2025-03-04T20:58:59.2044125Z ::
2025-03-04T20:58:59.2044328Z 
2025-03-04T20:58:59.2044719Z     >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2025-03-04T20:58:59.2045591Z     >>>                                      init_method='env://')
2025-03-04T20:58:59.2046067Z 
2025-03-04T20:58:59.2046523Z 4. In your training program, you can either use regular distributed functions
2025-03-04T20:58:59.2047650Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2025-03-04T20:58:59.2048666Z training program uses GPUs for training and you would like to use
2025-03-04T20:58:59.2049646Z :func:`torch.nn.parallel.DistributedDataParallel` module,
2025-03-04T20:58:59.2050502Z here is how to configure it.
2025-03-04T20:58:59.2050853Z 
2025-03-04T20:58:59.2051019Z ::
2025-03-04T20:58:59.2051238Z 
2025-03-04T20:58:59.2051598Z     >>> model = torch.nn.parallel.DistributedDataParallel(model,
2025-03-04T20:58:59.2052539Z     >>>                                                   device_ids=[args.local_rank],
2025-03-04T20:58:59.2053279Z     >>>                                                   output_device=args.local_rank)
2025-03-04T20:58:59.2053762Z 
2025-03-04T20:58:59.2054212Z Please ensure that ``device_ids`` argument is set to be the only GPU device id
2025-03-04T20:58:59.2055296Z that your code will be operating on. This is generally the local rank of the
2025-03-04T20:58:59.2056392Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2025-03-04T20:58:59.2057247Z and ``output_device`` needs to be ``args.local_rank`` in order to use this
2025-03-04T20:58:59.2058084Z utility
2025-03-04T20:58:59.2058305Z 
2025-03-04T20:58:59.2058763Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2025-03-04T20:58:59.2059833Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2025-03-04T20:58:59.2060907Z ``--use-env=True``. You must adjust the subprocess example above to replace
2025-03-04T20:58:59.2061919Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2025-03-04T20:58:59.2062824Z will not pass ``--local-rank`` when you specify this flag.
2025-03-04T20:58:59.2063359Z 
2025-03-04T20:58:59.2063552Z .. warning::
2025-03-04T20:58:59.2063791Z 
2025-03-04T20:58:59.2064175Z     ``local_rank`` is NOT globally unique: it is only unique per process
2025-03-04T20:58:59.2065119Z     on a machine.  Thus, don't use it to decide if you should, e.g.,
2025-03-04T20:58:59.2065912Z     write to a networked filesystem.  See
2025-03-04T20:58:59.2066709Z     https://github.com/pytorch/pytorch/issues/12042 for an example of
2025-03-04T20:58:59.2067630Z     how things can go wrong if you don't do this correctly.
2025-03-04T20:58:59.2068191Z 
2025-03-04T20:58:59.2068197Z 
2025-03-04T20:58:59.2068202Z 
2025-03-04T20:58:59.2068207Z 
2025-03-04T20:58:59.2068661Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.2069315Z 
2025-03-04T20:58:59.2644576Z 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.
2025-03-04T20:58:59.2646765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.2647505Z 
2025-03-04T20:58:59.2647990Z Creates an :class:`ShardedTensor` from local shards and the global metadata.
2025-03-04T20:58:59.2648921Z Needs to be called on all ranks in an SPMD fashion.
2025-03-04T20:58:59.2649404Z 
2025-03-04T20:58:59.2649582Z Args:
2025-03-04T20:58:59.2650309Z     local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2025-03-04T20:58:59.2651635Z         of shards that represent the local shards on this rank.
2025-03-04T20:58:59.2652623Z     global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2025-03-04T20:58:59.2653498Z         shape of the overall sharded tensor.
2025-03-04T20:58:59.2653918Z 
2025-03-04T20:58:59.2654113Z Keyword args:
2025-03-04T20:58:59.2654859Z     process_group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:58:59.2655696Z         the default process group will be used.
2025-03-04T20:58:59.2656131Z     init_rrefs (bool, optional): Whether or not to initialize
2025-03-04T20:58:59.2656651Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-04T20:58:59.2657198Z         Need to initialize the RPC Framework if specified as ``True``.
2025-03-04T20:58:59.2657631Z         Default: ``False``.
2025-03-04T20:58:59.2657926Z 
2025-03-04T20:58:59.2658033Z Returns:
2025-03-04T20:58:59.2658330Z     A :class:`ShardedTensor` object handle on this rank
2025-03-04T20:58:59.2658618Z 
2025-03-04T20:58:59.2658764Z 
2025-03-04T20:58:59.2658863Z Examples:
2025-03-04T20:58:59.2659275Z     Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2025-03-04T20:58:59.2660003Z     each shard have a (5, 5) local tensor, we can do it like below:
2025-03-04T20:58:59.2660326Z 
2025-03-04T20:58:59.2660430Z     on rank 0:
2025-03-04T20:58:59.2660709Z     >>> # xdoctest: +SKIP("not distributed")
2025-03-04T20:58:59.2661084Z     >>> local_shard_metadata = ShardMetadata(
2025-03-04T20:58:59.2661544Z     >>>     shard_offsets=[0, 0],
2025-03-04T20:58:59.2662070Z     >>>     shard_lengths=[5, 5],
2025-03-04T20:58:59.2662610Z     >>>     placement="rank:0/cuda:0"
2025-03-04T20:58:59.2663158Z     >>> )
2025-03-04T20:58:59.2663715Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-04T20:58:59.2664266Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-04T20:58:59.2664596Z 
2025-03-04T20:58:59.2664705Z     on rank 1:
2025-03-04T20:58:59.2664992Z     >>> # xdoctest: +SKIP("not distributed")
2025-03-04T20:58:59.2665368Z     >>> local_shard_metadata = ShardMetadata(
2025-03-04T20:58:59.2665718Z     >>>     shard_offsets=[5, 0],
2025-03-04T20:58:59.2666051Z     >>>     shard_lengths=[5, 5],
2025-03-04T20:58:59.2666387Z     >>>     placement="rank:1/cuda:1"
2025-03-04T20:58:59.2666706Z     >>> )
2025-03-04T20:58:59.2667050Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-04T20:58:59.2667583Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-04T20:58:59.2667902Z 
2025-03-04T20:58:59.2668180Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.2668564Z 
2025-03-04T20:58:59.2778816Z 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=799.
2025-03-04T20:58:59.2781096Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.2781818Z 
2025-03-04T20:58:59.2782305Z Initialize a ShardedTensor given only one local tensor, global sharded tensor
2025-03-04T20:58:59.2783237Z size and sharding spec on each rank.
2025-03-04T20:58:59.2783664Z 
2025-03-04T20:58:59.2783834Z Args:
2025-03-04T20:58:59.2784453Z     local_tensor (Tensor): Single tensor of local shard stored in each rank.
2025-03-04T20:58:59.2785640Z     sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2025-03-04T20:58:59.2786694Z         The specification describing how to shard the Tensor.
2025-03-04T20:58:59.2787523Z     global_size (Sequence[int]): Size of the sharded tensor.
2025-03-04T20:58:59.2788440Z     process_group (ProcessGroup, optional): The process group to aggregate on.
2025-03-04T20:58:59.2789309Z         Default: None
2025-03-04T20:58:59.2789922Z     init_rrefs (bool, optional): Whether or not to initialize
2025-03-04T20:58:59.2791049Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-04T20:58:59.2792051Z         Need to initialize the RPC Framework if specified as ``True``.
2025-03-04T20:58:59.2792726Z         Default: ``False``.
2025-03-04T20:58:59.2793064Z 
2025-03-04T20:58:59.2793245Z Returns:
2025-03-04T20:58:59.2793881Z     A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2025-03-04T20:58:59.2794431Z         tensor stored in the current rank.
2025-03-04T20:58:59.2794679Z 
2025-03-04T20:58:59.2794776Z Examples:
2025-03-04T20:58:59.2795022Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.2795351Z     >>> # All tensors below are of torch.int64 type.
2025-03-04T20:58:59.2795744Z     >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:58:59.2796173Z     >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2025-03-04T20:58:59.2796690Z     >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2025-03-04T20:58:59.2797128Z     >>> local_tensor
2025-03-04T20:58:59.2797403Z     tensor([[1, 2, 3, 4]]) # Rank 0
2025-03-04T20:58:59.2797840Z     tensor([[3, 4, 5, 6]]) # Rank 1
2025-03-04T20:58:59.2798154Z     >>> sharding_dim = 0
2025-03-04T20:58:59.2798467Z     >>> sharding_spec = ChunkShardingSpec(
2025-03-04T20:58:59.2798819Z             dim=sharding_dim,
2025-03-04T20:58:59.2799220Z             placements=[
2025-03-04T20:58:59.2799582Z                 "rank:0/cuda:0",
2025-03-04T20:58:59.2800065Z                 "rank:1/cuda:1",
2025-03-04T20:58:59.2800536Z             ],
2025-03-04T20:58:59.2800940Z         )
2025-03-04T20:58:59.2801394Z     >>> st = ShardedTensor._init_from_local_tensor(
2025-03-04T20:58:59.2802090Z     ...     local_tensor, sharding_spec, [2, 4]
2025-03-04T20:58:59.2802689Z     ... )
2025-03-04T20:58:59.2803066Z     >>> st
2025-03-04T20:58:59.2803446Z     ShardedTensor(
2025-03-04T20:58:59.2803872Z         ShardedTensorMetadata(
2025-03-04T20:58:59.2804424Z             shards_metadata=[
2025-03-04T20:58:59.2805200Z                 ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2025-03-04T20:58:59.2805888Z                 ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2025-03-04T20:58:59.2806375Z             ],
2025-03-04T20:58:59.2806629Z             size=torch.Size([2, 4])
2025-03-04T20:58:59.2806944Z     )
2025-03-04T20:58:59.2807176Z     >>> st.local_tensor()
2025-03-04T20:58:59.2807459Z     tensor([1, 2, 3, 4]) # Rank 0
2025-03-04T20:58:59.2807751Z     tensor([3, 4, 5, 6]) # Rank 1
2025-03-04T20:58:59.2807961Z 
2025-03-04T20:58:59.2808239Z Warning: This API is experimental and subject to change. It lacks of a fully across
2025-03-04T20:58:59.2808891Z          rank validations, and we only validate the local shard on the current rank.
2025-03-04T20:58:59.2809484Z          We fully rely on the user to ensure local tensor is sharded based on the
2025-03-04T20:58:59.2809933Z          sharding spec.
2025-03-04T20:58:59.2810117Z 
2025-03-04T20:58:59.2810379Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.2810936Z 
2025-03-04T20:58:59.2812264Z 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=1040.
2025-03-04T20:58:59.2814002Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.2814396Z 
2025-03-04T20:58:59.2814673Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2025-03-04T20:58:59.2815164Z single local shard.
2025-03-04T20:58:59.2815322Z 
2025-03-04T20:58:59.2815564Z If ``resharding_spec`` is same as the original one, this becomes a no-op.
2025-03-04T20:58:59.2816168Z If only ``resharding_spec`` shares the same sharding dim with the original one,
2025-03-04T20:58:59.2816663Z we swap local shards directly.
2025-03-04T20:58:59.2817136Z For more generic cases, we merge different shards across different ranks and split
2025-03-04T20:58:59.2817981Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2025-03-04T20:58:59.2818363Z 
2025-03-04T20:58:59.2818467Z Args:
2025-03-04T20:58:59.2819133Z     resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2025-03-04T20:58:59.2820211Z         specification describing how the tensor is sharded.
2025-03-04T20:58:59.2820751Z 
2025-03-04T20:58:59.2820913Z Returns:
2025-03-04T20:58:59.2821521Z     A :class:`ShardedTensor` object whose local shards are resharded.
2025-03-04T20:58:59.2822151Z 
2025-03-04T20:58:59.2822315Z Examples:
2025-03-04T20:58:59.2822728Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.2823282Z     >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:58:59.2824052Z     >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2025-03-04T20:58:59.2824818Z     >>> tensor = torch.stack([tensor, tensor])
2025-03-04T20:58:59.2825379Z     >>> tensor
2025-03-04T20:58:59.2825864Z     tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2025-03-04T20:58:59.2826514Z     tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2025-03-04T20:58:59.2827253Z     tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2025-03-04T20:58:59.2827881Z     tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2025-03-04T20:58:59.2828515Z     >>> sharding_dim = 0
2025-03-04T20:58:59.2829171Z     >>> spec = ChunkShardingSpec(
2025-03-04T20:58:59.2829721Z             dim=sharding_dim,
2025-03-04T20:58:59.2830227Z             placements=[
2025-03-04T20:58:59.2830705Z                 "rank:0/cuda:0",
2025-03-04T20:58:59.2831206Z                 "rank:1/cuda:1",
2025-03-04T20:58:59.2831737Z                 "rank:2/cuda:2",
2025-03-04T20:58:59.2832233Z                 "rank:3/cuda:3",
2025-03-04T20:58:59.2832572Z             ],
2025-03-04T20:58:59.2832815Z         )
2025-03-04T20:58:59.2833066Z     >>> current_offsets = [0] * 2
2025-03-04T20:58:59.2833396Z     >>> current_offsets[0] = rank * 2
2025-03-04T20:58:59.2833758Z     >>> shard_metadata = ShardMetadata(
2025-03-04T20:58:59.2834155Z             shard_offsets=copy.deepcopy(current_offsets),
2025-03-04T20:58:59.2834556Z             shard_sizes=tensor.size(),
2025-03-04T20:58:59.2834921Z             placement=spec.placements[rank],
2025-03-04T20:58:59.2835260Z         )
2025-03-04T20:58:59.2835495Z     >>> local_shards = [
2025-03-04T20:58:59.2835778Z             Shard(
2025-03-04T20:58:59.2836034Z                 tensor=tensor,
2025-03-04T20:58:59.2836334Z                 metadata=shard_metadata,
2025-03-04T20:58:59.2836654Z             )
2025-03-04T20:58:59.2836882Z         ]
2025-03-04T20:58:59.2837255Z     >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2025-03-04T20:58:59.2837799Z     >>> sharding_dim = 1
2025-03-04T20:58:59.2838324Z     >>> resharding_spec = ChunkShardingSpec(
2025-03-04T20:58:59.2838900Z             dim=sharding_dim,
2025-03-04T20:58:59.2839412Z             placements=[
2025-03-04T20:58:59.2839902Z                 "rank:0/cuda:0",
2025-03-04T20:58:59.2840425Z                 "rank:1/cuda:1",
2025-03-04T20:58:59.2840944Z                 "rank:2/cuda:2",
2025-03-04T20:58:59.2841467Z                 "rank:3/cuda:3",
2025-03-04T20:58:59.2841980Z             ],
2025-03-04T20:58:59.2842387Z         )
2025-03-04T20:58:59.2842816Z     >>> st.reshard(resharding_spec)
2025-03-04T20:58:59.2843411Z     >>> tensor = st.local_shards()[0].tensor
2025-03-04T20:58:59.2843999Z     >>> tensor
2025-03-04T20:58:59.2844509Z     tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2025-03-04T20:58:59.2845248Z     tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2025-03-04T20:58:59.2845984Z     tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2025-03-04T20:58:59.2846728Z     tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2025-03-04T20:58:59.2847234Z 
2025-03-04T20:58:59.2847696Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.2848415Z 
2025-03-04T20:58:59.3010209Z 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.
2025-03-04T20:58:59.3011503Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.3011919Z 
2025-03-04T20:58:59.3012163Z Representation of a sharding plan, describes how to shard a module
2025-03-04T20:58:59.3012800Z across hosts. `plan` is used to shard module parameters according to the spec provided,
2025-03-04T20:58:59.3013504Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2025-03-04T20:58:59.3014181Z layout of a module with a spec, and when to convert back to data parallel fashion.
2025-03-04T20:58:59.3014568Z 
2025-03-04T20:58:59.3014662Z Args:
2025-03-04T20:58:59.3015071Z     plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2025-03-04T20:58:59.3015657Z           :class:`torch.distributed._shard.sharder.Sharder`]):
2025-03-04T20:58:59.3016503Z         a dict describes how to shard a module, there're currently two ways to shard a module:
2025-03-04T20:58:59.3017958Z             1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2025-03-04T20:58:59.3018911Z                a parameter to a `ShardingSpec`.
2025-03-04T20:58:59.3020072Z             2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2025-03-04T20:58:59.3021002Z                to a `Sharder` object.
2025-03-04T20:58:59.3022021Z     output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2025-03-04T20:58:59.3023386Z         a dict specifies the layout of a module's output which produces a ShardedTensor,
2025-03-04T20:58:59.3024564Z         keyed by the name of module to ShardingSpec("" in key means the root module).
2025-03-04T20:58:59.3025433Z         Default: `None`
2025-03-04T20:58:59.3026237Z     return_local_tensor (List[str], optional): a list of string, each element enables
2025-03-04T20:58:59.3027461Z         a module's sharded output to be returned as a Tensor from its local shards to
2025-03-04T20:58:59.3028664Z         ensure further processing in a data parallel fashion. ("" in list means the
2025-03-04T20:58:59.3029493Z         root module).
2025-03-04T20:58:59.3029943Z         Default: None
2025-03-04T20:58:59.3030399Z Example:
2025-03-04T20:58:59.3031194Z   Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2025-03-04T20:58:59.3032469Z   want to convert the output of the second linear layer back to DDP, we can do it as follows:
2025-03-04T20:58:59.3033237Z 
2025-03-04T20:58:59.3033572Z     >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2025-03-04T20:58:59.3034316Z     >>> class MyModule(nn.Module):
2025-03-04T20:58:59.3034910Z     >>>     def __init__(self) -> None:
2025-03-04T20:58:59.3035495Z     >>>        super().__init__()
2025-03-04T20:58:59.3036059Z     >>>        self.fc1 = nn.Linear()
2025-03-04T20:58:59.3036646Z     >>>        self.gelu = nn.GELU()
2025-03-04T20:58:59.3037233Z     >>>        self.fc2 = nn.Linear()
2025-03-04T20:58:59.3037852Z     >>>        self.relu = nn.Linear()
2025-03-04T20:58:59.3038417Z     >>>
2025-03-04T20:58:59.3038840Z     >>>     def forward(self, input):
2025-03-04T20:58:59.3039582Z     >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2025-03-04T20:58:59.3040157Z 
2025-03-04T20:58:59.3040165Z 
2025-03-04T20:58:59.3040411Z     >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2025-03-04T20:58:59.3041092Z     >>> sharding_plan = ShardingPlan(
2025-03-04T20:58:59.3041627Z     >>>    plan={
2025-03-04T20:58:59.3042071Z     >>>        "fc1.weight": spec1,
2025-03-04T20:58:59.3042602Z     >>>        "fc2.weight": spec2
2025-03-04T20:58:59.3042959Z     >>>    },
2025-03-04T20:58:59.3043213Z     >>>    output_plan={
2025-03-04T20:58:59.3043516Z     >>>        "fc2": output_spec
2025-03-04T20:58:59.3043815Z     >>>    },
2025-03-04T20:58:59.3044078Z     >>>    return_local_tensor=["fc2"]
2025-03-04T20:58:59.3044566Z     >>> )
2025-03-04T20:58:59.3044710Z 
2025-03-04T20:58:59.3044977Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.3045378Z 
2025-03-04T20:58:59.3856968Z 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.
2025-03-04T20:58:59.3858255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.3858651Z 
2025-03-04T20:58:59.3858787Z Run post-localSGD algorithm.
2025-03-04T20:58:59.3858992Z 
2025-03-04T20:58:59.3859254Z This DDP communication hook is used for running post-localSGD algorithm,
2025-03-04T20:58:59.3859796Z by combining with a model averaging component (e.g.,
2025-03-04T20:58:59.3860422Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2025-03-04T20:58:59.3861037Z that runs after the optimizer step.
2025-03-04T20:58:59.3861421Z 
2025-03-04T20:58:59.3861577Z Args:
2025-03-04T20:58:59.3862209Z     state (PostLocalSGDState): State information to run post-localSGD.
2025-03-04T20:58:59.3863565Z         Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2025-03-04T20:58:59.3865321Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-04T20:58:59.3866845Z         Note that since DDP comm hook only supports single process single device mode,
2025-03-04T20:58:59.3867832Z         only exactly one tensor is stored in this bucket.
2025-03-04T20:58:59.3868355Z 
2025-03-04T20:58:59.3868516Z Returns:
2025-03-04T20:58:59.3869227Z     Future handler of the communication, which updates the gradients in place.
2025-03-04T20:58:59.3869938Z 
2025-03-04T20:58:59.3870130Z Example::
2025-03-04T20:58:59.3870547Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.3871360Z     >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2025-03-04T20:58:59.3872309Z                               start_localSGD_iter=10)
2025-03-04T20:58:59.3873044Z     >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:58:59.3874425Z     >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2025-03-04T20:58:59.3875983Z     >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2025-03-04T20:58:59.3876882Z 
2025-03-04T20:58:59.3877353Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.3878082Z 
2025-03-04T20:58:59.3916587Z 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.
2025-03-04T20:58:59.3918682Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.3919413Z 
2025-03-04T20:58:59.3919633Z Implement PowerSGD algorithm.
2025-03-04T20:58:59.3920000Z 
2025-03-04T20:58:59.3920416Z This DDP communication hook implements PowerSGD gradient compression
2025-03-04T20:58:59.3921488Z algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2025-03-04T20:58:59.3922600Z Once gradient tensors are aggregated across all workers, this hook applies
2025-03-04T20:58:59.3923183Z compression as follows:
2025-03-04T20:58:59.3923377Z 
2025-03-04T20:58:59.3923831Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2025-03-04T20:58:59.3924410Z 
2025-03-04T20:58:59.3924847Z     1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2025-03-04T20:58:59.3925400Z 
2025-03-04T20:58:59.3925819Z     1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2025-03-04T20:58:59.3926364Z 
2025-03-04T20:58:59.3926690Z 2. Handles uncompressed tensors:
2025-03-04T20:58:59.3926913Z 
2025-03-04T20:58:59.3927440Z     2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2025-03-04T20:58:59.3928097Z 
2025-03-04T20:58:59.3928451Z     2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2025-03-04T20:58:59.3928935Z 
2025-03-04T20:58:59.3929179Z 3. Handles the tensors that should be compressed by PowerSGD compression:
2025-03-04T20:58:59.3929551Z 
2025-03-04T20:58:59.3929799Z     3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2025-03-04T20:58:59.3930505Z     such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2025-03-04T20:58:59.3930951Z 
2025-03-04T20:58:59.3931108Z     3.2. Computes each P in Ps, which is equal to MQ;
2025-03-04T20:58:59.3931384Z 
2025-03-04T20:58:59.3931502Z     3.3. Allreduces Ps as a batch;
2025-03-04T20:58:59.3931728Z 
2025-03-04T20:58:59.3931851Z     3.4. Orthogonalizes each P in Ps;
2025-03-04T20:58:59.3932143Z 
2025-03-04T20:58:59.3932350Z     3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2025-03-04T20:58:59.3932680Z 
2025-03-04T20:58:59.3932792Z     3.6. Allreduces Qs as a batch;
2025-03-04T20:58:59.3933086Z 
2025-03-04T20:58:59.3933747Z     3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2025-03-04T20:58:59.3934550Z 
2025-03-04T20:58:59.3935346Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2025-03-04T20:58:59.3936908Z This not only gives the user more control over the tradeoff between speedup and accuracy,
2025-03-04T20:58:59.3938599Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2025-03-04T20:58:59.3939683Z 
2025-03-04T20:58:59.3939843Z Args:
2025-03-04T20:58:59.3940921Z     state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2025-03-04T20:58:59.3942685Z         To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2025-03-04T20:58:59.3943834Z         and ``min_compression_rate``.
2025-03-04T20:58:59.3945091Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-04T20:58:59.3946657Z         Note that since DDP comm hook only supports single process single device mode,
2025-03-04T20:58:59.3947627Z         only exactly one tensor is stored in this bucket.
2025-03-04T20:58:59.3948103Z 
2025-03-04T20:58:59.3948268Z Returns:
2025-03-04T20:58:59.3948968Z     Future handler of the communication, which updates the gradients in place.
2025-03-04T20:58:59.3949669Z 
2025-03-04T20:58:59.3949854Z Example::
2025-03-04T20:58:59.3950281Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.3951112Z     >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2025-03-04T20:58:59.3952115Z                               start_powerSGD_iter=10, min_compression_rate=0.5)
2025-03-04T20:58:59.3952919Z     >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2025-03-04T20:58:59.3953410Z 
2025-03-04T20:58:59.3953910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.3954585Z 
2025-03-04T20:58:59.3984567Z 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.
2025-03-04T20:58:59.3985909Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.3986319Z 
2025-03-04T20:58:59.3986523Z Averages parameters periodically after the warm-up stage.
2025-03-04T20:58:59.3986847Z 
2025-03-04T20:58:59.3987115Z This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-04T20:58:59.3987828Z by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2025-03-04T20:58:59.3988395Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2025-03-04T20:58:59.3988779Z 
2025-03-04T20:58:59.3988873Z Args:
2025-03-04T20:58:59.3989187Z     period (int): The number of steps per model averaging.
2025-03-04T20:58:59.3989762Z                   Usually the period should be greater than ``1`` to reduce the communication cost.
2025-03-04T20:58:59.3990304Z                   Otherwise, only DDP needs to be used.
2025-03-04T20:58:59.3990850Z     warmup_steps (int): The number of warm-up steps. During this stage,
2025-03-04T20:58:59.3991655Z                         model averaging is skipped.
2025-03-04T20:58:59.3992432Z     process_group: The process group to be used for all-reduce.
2025-03-04T20:58:59.3993259Z                    If ``None``, the default process group, which
2025-03-04T20:58:59.3994093Z                    is created by :func:`torch.distributed.init_process_group`,
2025-03-04T20:58:59.3994924Z                    will be used. (default: ``None``)
2025-03-04T20:58:59.3995368Z 
2025-03-04T20:58:59.3995711Z Example::
2025-03-04T20:58:59.3995942Z 
2025-03-04T20:58:59.3996190Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.3996831Z     >>> import torch
2025-03-04T20:58:59.3997483Z     >>> import torch.distributed as dist
2025-03-04T20:58:59.3998472Z     >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2025-03-04T20:58:59.3999808Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-04T20:58:59.4000768Z     >>> import torch.nn as nn
2025-03-04T20:58:59.4001303Z     >>>
2025-03-04T20:58:59.4001890Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-04T20:58:59.4002687Z     >>> torch.cuda.set_device(rank)
2025-03-04T20:58:59.4003330Z     >>> module = nn.Linear(1, 1, bias=False).cuda()
2025-03-04T20:58:59.4004070Z     >>> model = nn.parallel.DistributedDataParallel(
2025-03-04T20:58:59.4004855Z     >>>    module, device_ids=[rank], output_device=rank
2025-03-04T20:58:59.4005516Z     >>> )
2025-03-04T20:58:59.4006032Z     >>> # Register a post-localSGD communication hook.
2025-03-04T20:58:59.4007136Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-04T20:58:59.4008198Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:58:59.4008889Z     >>>
2025-03-04T20:58:59.4009590Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-04T20:58:59.4010607Z     >>> # After 100 steps, run model averaging every 4 steps.
2025-03-04T20:58:59.4011744Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-04T20:58:59.4013070Z     >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-04T20:58:59.4013911Z     >>> for step in range(0, 200):
2025-03-04T20:58:59.4014418Z     >>>    optimizer.zero_grad()
2025-03-04T20:58:59.4014904Z     >>>    loss = loss_fn(output, labels)
2025-03-04T20:58:59.4015442Z     >>>    loss.backward()
2025-03-04T20:58:59.4015954Z     >>>    optimizer.step()
2025-03-04T20:58:59.4016640Z     >>>    # Will average model parameters globally every 4 steps. Thus,
2025-03-04T20:58:59.4017635Z     >>>    # inter-node communication only occurs every 4 iterations after
2025-03-04T20:58:59.4018549Z     >>>    # the initial ``warmup_steps`` period.
2025-03-04T20:58:59.4019232Z     >>>    averager.average_parameters(model.parameters())
2025-03-04T20:58:59.4019760Z 
2025-03-04T20:58:59.4020207Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.4020640Z 
2025-03-04T20:58:59.4021555Z 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.
2025-03-04T20:58:59.4022890Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.4023278Z 
2025-03-04T20:58:59.4023652Z Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2025-03-04T20:58:59.4024116Z 
2025-03-04T20:58:59.4024451Z Process groups of different sizes are organized in a hierarchy, and they average parameters
2025-03-04T20:58:59.4025111Z by using different periods concurrently after the warm-up stage.
2025-03-04T20:58:59.4025859Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2025-03-04T20:58:59.4027290Z that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2025-03-04T20:58:59.4028715Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2025-03-04T20:58:59.4030212Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2025-03-04T20:58:59.4031732Z Similarly, the process groups within this class do not have such an intra-machine process
2025-03-04T20:58:59.4033137Z subgroup, which should be embedded by the post-local SGD communication hook instead.
2025-03-04T20:58:59.4033889Z 
2025-03-04T20:58:59.4034064Z Args:
2025-03-04T20:58:59.4034869Z     period_group_size_dict: An ordered dict mapping keys of model averaging period to
2025-03-04T20:58:59.4036003Z                             process group size, used for initializing process groups of
2025-03-04T20:58:59.4037002Z                             different sizes in a hierarchy to average parameters concurrently.
2025-03-04T20:58:59.4038035Z                             Particularly, at each iteration, there will be at most a single
2025-03-04T20:58:59.4038783Z                             process group that runs averaging -- the period of such group should
2025-03-04T20:58:59.4039378Z                             have the largest period which the current step can be divided by.
2025-03-04T20:58:59.4039913Z                             For example, if the dict has three keys: 2, 4, and 8,
2025-03-04T20:58:59.4040435Z                             then this means totally three process groups will be created to
2025-03-04T20:58:59.4041002Z                             average parameters every 2, 4, and 8 iterations, respectively.
2025-03-04T20:58:59.4041551Z                             At the 4th iteration, only the second process group will run
2025-03-04T20:58:59.4042060Z                             averaging, because the first process group should be a
2025-03-04T20:58:59.4042599Z                             subset of the second process group, and no need to execute the first
2025-03-04T20:58:59.4043082Z                             process group redundantly.
2025-03-04T20:58:59.4043540Z                             On the other hand, the third process group can only be triggered
2025-03-04T20:58:59.4044421Z                             every 8 iterations, so it will not be triggered at the 4th iteration.
2025-03-04T20:58:59.4045620Z     warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2025-03-04T20:58:59.4047288Z     process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2025-03-04T20:58:59.4048710Z                                             If ``None``, the default process group, which is created
2025-03-04T20:58:59.4049601Z                                             by :func:`torch.distributed.init_process_group`, will be used.
2025-03-04T20:58:59.4050459Z                                             (default: ``None``)
2025-03-04T20:58:59.4050908Z 
2025-03-04T20:58:59.4051087Z Example::
2025-03-04T20:58:59.4051528Z     >>> # xdoctest: +SKIP('undefined rank')
2025-03-04T20:58:59.4052165Z     >>> from collections import OrderedDict
2025-03-04T20:58:59.4052779Z     >>> import torch
2025-03-04T20:58:59.4053293Z     >>> import torch.distributed as dist
2025-03-04T20:58:59.4054244Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-04T20:58:59.4055313Z     >>>     PostLocalSGDState,
2025-03-04T20:58:59.4055873Z     >>>     post_localSGD_hook,
2025-03-04T20:58:59.4056385Z     >>> )
2025-03-04T20:58:59.4057290Z     >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2025-03-04T20:58:59.4058530Z     >>> import torch.nn as nn
2025-03-04T20:58:59.4058895Z     >>>
2025-03-04T20:58:59.4059224Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-04T20:58:59.4059649Z     >>> torch.cuda.set_device(rank)
2025-03-04T20:58:59.4060016Z     >>> module = nn.Linear(1, 1, bias=False).to(rank)
2025-03-04T20:58:59.4060440Z     >>> model = nn.parallel.DistributedDataParallel(
2025-03-04T20:58:59.4060871Z     >>>    module, device_ids=[rank], output_device=rank
2025-03-04T20:58:59.4061232Z     >>> )
2025-03-04T20:58:59.4061523Z     >>> # Register a post-localSGD communication hook.
2025-03-04T20:58:59.4062081Z     >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2025-03-04T20:58:59.4062617Z     >>> subgroup, _ = dist.new_subgroups()
2025-03-04T20:58:59.4063306Z     >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2025-03-04T20:58:59.4063928Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:58:59.4064537Z     >>>
2025-03-04T20:58:59.4065272Z     >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2025-03-04T20:58:59.4066251Z     >>> # the 16 processes every 16 iterations.
2025-03-04T20:58:59.4067027Z     >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2025-03-04T20:58:59.4068038Z     >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2025-03-04T20:58:59.4069321Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-04T20:58:59.4070603Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-04T20:58:59.4071594Z     >>> # After 100 steps, run model averaging at two levels.
2025-03-04T20:58:59.4072257Z     >>> for step in range(0, 200):
2025-03-04T20:58:59.4072815Z     >>>    optimizer.zero_grad()
2025-03-04T20:58:59.4073263Z     >>>    loss = loss_fn(output, labels)
2025-03-04T20:58:59.4073855Z     >>>    loss.backward()
2025-03-04T20:58:59.4074163Z     >>>    optimizer.step()
2025-03-04T20:58:59.4074579Z     >>>    # Average parameters after ``optimizer.step()``.
2025-03-04T20:58:59.4075223Z     >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2025-03-04T20:58:59.4075814Z     >>>    averager.average_parameters(model.parameters())
2025-03-04T20:58:59.4076111Z 
2025-03-04T20:58:59.4076220Z .. warning ::
2025-03-04T20:58:59.4076629Z     The last group size in the dict must be the size of the provided ``process_group``,
2025-03-04T20:58:59.4077255Z     which indicates model averaging at the highest level of the hierarchy.
2025-03-04T20:58:59.4077936Z     If ``process_group`` is not provided, then the last group size should be equal to the world size.
2025-03-04T20:58:59.4078383Z 
2025-03-04T20:58:59.4078482Z .. warning ::
2025-03-04T20:58:59.4078954Z     `HierarchicalModelAverager` is experimental and subject to change.
2025-03-04T20:58:59.4079591Z 
2025-03-04T20:58:59.4080060Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.4080775Z 
2025-03-04T20:58:59.6064406Z 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.
2025-03-04T20:58:59.6066494Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6067247Z 
2025-03-04T20:58:59.6067824Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2025-03-04T20:58:59.6069114Z on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2025-03-04T20:58:59.6070026Z 
2025-03-04T20:58:59.6070295Z . N.B. Intended to be used with DynamicMetaLoadPlanner
2025-03-04T20:58:59.6070802Z 
2025-03-04T20:58:59.6070994Z .. warning::
2025-03-04T20:58:59.6071502Z     Current implementation only supports loading Tensors.
2025-03-04T20:58:59.6071891Z 
2025-03-04T20:58:59.6072047Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6072388Z >>> sd = {"mode": model}
2025-03-04T20:58:59.6072665Z >>> dcp.load(
2025-03-04T20:58:59.6072903Z >>>    sd,
2025-03-04T20:58:59.6073185Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-04T20:58:59.6073842Z >>>    planner=DynamicMetaLoadPlanner(),
2025-03-04T20:58:59.6074218Z >>>    checkpoint_id="path_to_model.pt"
2025-03-04T20:58:59.6074547Z >>> )
2025-03-04T20:58:59.6074684Z 
2025-03-04T20:58:59.6074949Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6075338Z 
2025-03-04T20:58:59.6076070Z 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.
2025-03-04T20:58:59.6077281Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6077683Z 
2025-03-04T20:58:59.6078168Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2025-03-04T20:58:59.6079506Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2025-03-04T20:58:59.6080552Z metadata file, like Torch Save files.
2025-03-04T20:58:59.6080973Z 
2025-03-04T20:58:59.6081297Z . N.B. Intended to be used with BroadcastingTorchSaveReader
2025-03-04T20:58:59.6081856Z 
2025-03-04T20:58:59.6082032Z .. warning::
2025-03-04T20:58:59.6082594Z     Current implementation only supports loading Tensors.
2025-03-04T20:58:59.6083134Z 
2025-03-04T20:58:59.6083315Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6083928Z >>> sd = {"mode": model}
2025-03-04T20:58:59.6084389Z >>> dcp.load(
2025-03-04T20:58:59.6084786Z >>>    sd,
2025-03-04T20:58:59.6085299Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-04T20:58:59.6086047Z >>>    planner=DynamicMetaLoadPlanner(),
2025-03-04T20:58:59.6086679Z >>>    checkpoint_id="path_to_model.pt"
2025-03-04T20:58:59.6087247Z >>> )
2025-03-04T20:58:59.6087464Z 
2025-03-04T20:58:59.6087950Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6088614Z 
2025-03-04T20:58:59.6154202Z 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.
2025-03-04T20:58:59.6155362Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6155773Z 
2025-03-04T20:58:59.6156004Z Load a state_dict in conjunction with FSDP sharded optimizer state.
2025-03-04T20:58:59.6156368Z 
2025-03-04T20:58:59.6156542Z This is the current recommended way to checkpoint FSDP.
2025-03-04T20:58:59.6156941Z >>> # xdoctest: +SKIP
2025-03-04T20:58:59.6157284Z >>> import torch.distributed.checkpoint as dist_cp
2025-03-04T20:58:59.6157664Z >>> # Save
2025-03-04T20:58:59.6157910Z >>> model: torch.nn.Model
2025-03-04T20:58:59.6158222Z >>> optim_params = model.parameters()
2025-03-04T20:58:59.6158617Z >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2025-03-04T20:58:59.6159012Z >>> # Save
2025-03-04T20:58:59.6159614Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2025-03-04T20:58:59.6160382Z >>>     state_dict = {
2025-03-04T20:58:59.6160938Z >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2025-03-04T20:58:59.6161640Z >>>         "model": model.state_dict()
2025-03-04T20:58:59.6162207Z >>>     }
2025-03-04T20:58:59.6162630Z >>>     dist_cp.save_state_dict(
2025-03-04T20:58:59.6163193Z >>>         state_dict=optim_state,
2025-03-04T20:58:59.6163904Z >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2025-03-04T20:58:59.6164901Z >>>         planner=dist_cp.DefaultSavePlanner(),
2025-03-04T20:58:59.6165543Z >>>     )
2025-03-04T20:58:59.6165921Z >>>
2025-03-04T20:58:59.6166288Z >>> # Load
2025-03-04T20:58:59.6166939Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2025-03-04T20:58:59.6167837Z >>>     model_state_dict = model_tp.state_dict()
2025-03-04T20:58:59.6168481Z >>>     checkpoint = {
2025-03-04T20:58:59.6168985Z >>>         "model": model_state_dict
2025-03-04T20:58:59.6169538Z >>>     }
2025-03-04T20:58:59.6169957Z >>>     dist_cp.load_state_dict(
2025-03-04T20:58:59.6170479Z >>>         state_dict=checkpoint,
2025-03-04T20:58:59.6171220Z >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2025-03-04T20:58:59.6172062Z >>>         planner=dist_cp.DefaultLoadPlanner(),
2025-03-04T20:58:59.6172667Z >>>     )
2025-03-04T20:58:59.6173207Z >>>     model.load_state_dict(checkpoint["model_state"])
2025-03-04T20:58:59.6174107Z >>>
2025-03-04T20:58:59.6174667Z >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2025-03-04T20:58:59.6175599Z >>>         model_state_dict,
2025-03-04T20:58:59.6176146Z >>>         optimizer_key="optimizer",
2025-03-04T20:58:59.6176894Z >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2025-03-04T20:58:59.6177579Z >>>     )
2025-03-04T20:58:59.6178180Z >>>
2025-03-04T20:58:59.6178625Z >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2025-03-04T20:58:59.6179312Z >>>        model, optim, optim_state["optimizer"]
2025-03-04T20:58:59.6179920Z >>>     )
2025-03-04T20:58:59.6180292Z >>>
2025-03-04T20:58:59.6180729Z >>>     optim.load_state_dict(flattened_osd)
2025-03-04T20:58:59.6181176Z 
2025-03-04T20:58:59.6181657Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6182373Z 
2025-03-04T20:58:59.6195659Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=113.
2025-03-04T20:58:59.6197635Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6198393Z 
2025-03-04T20:58:59.6198966Z Abstract class defining the protocol used by save_state_dict to plan the save process.
2025-03-04T20:58:59.6199786Z 
2025-03-04T20:58:59.6200399Z SavePlanners are stateful objects that can be used to customize the whole save process.
2025-03-04T20:58:59.6201218Z 
2025-03-04T20:58:59.6201776Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-04T20:58:59.6202791Z will be visible to the whole process.
2025-03-04T20:58:59.6203214Z 
2025-03-04T20:58:59.6203768Z A planner subclass can expect the following sequence of calls during save_state_dict:
2025-03-04T20:58:59.6204518Z 
2025-03-04T20:58:59.6204731Z 1) set_up_planner - called on all ranks.
2025-03-04T20:58:59.6205339Z     Signals the start of a checkpoint save.
2025-03-04T20:58:59.6205712Z 
2025-03-04T20:58:59.6205942Z 2) create_local_plan - called on all ranks.
2025-03-04T20:58:59.6206855Z     Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2025-03-04T20:58:59.6207631Z 
2025-03-04T20:58:59.6207990Z 3) create_global_plan - called on the coordinator rank only.
2025-03-04T20:58:59.6208947Z     Takes the SavePlan from all ranks and make any global decision.
2025-03-04T20:58:59.6209539Z 
2025-03-04T20:58:59.6209774Z 4) finish_plan - called on all ranks.
2025-03-04T20:58:59.6210606Z     This gives each rank a chance to adjust to global planning decisions.
2025-03-04T20:58:59.6211253Z 
2025-03-04T20:58:59.6211550Z 5) resolve_data - called multiple times on each rank
2025-03-04T20:58:59.6212453Z     Lookups a value on the `state_dict` for the storage layer to write.
2025-03-04T20:58:59.6213075Z 
2025-03-04T20:58:59.6213671Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2025-03-04T20:58:59.6214779Z most changes can be expressed by changes in a single method.
2025-03-04T20:58:59.6215437Z 
2025-03-04T20:58:59.6215591Z There are 3 usual patterns of extension:
2025-03-04T20:58:59.6215836Z 
2025-03-04T20:58:59.6216114Z Rewriting state_dict. This is the simplest way to extend the save process as it
2025-03-04T20:58:59.6216745Z doesn't requite understanding the intrincacies of how SavePlan works:
2025-03-04T20:58:59.6217098Z 
2025-03-04T20:58:59.6217245Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6217628Z >>> class RenamePlanner(DefaultSavePlanner):
2025-03-04T20:58:59.6218085Z >>>     def set_up_planner(
2025-03-04T20:58:59.6218381Z >>>         self,
2025-03-04T20:58:59.6218658Z >>>         state_dict: STATE_DICT_TYPE,
2025-03-04T20:58:59.6219026Z >>>         storage_meta: Optional[StorageMeta],
2025-03-04T20:58:59.6219392Z >>>         is_coordinator: bool,
2025-03-04T20:58:59.6219704Z >>>     ) -> None:
2025-03-04T20:58:59.6219976Z >>> # prefix all keys with `foo_``
2025-03-04T20:58:59.6220494Z >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2025-03-04T20:58:59.6220921Z 
2025-03-04T20:58:59.6221329Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2025-03-04T20:58:59.6221991Z 
2025-03-04T20:58:59.6222202Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6222919Z >>> class FP16Planner(DefaultSavePlanner):
2025-03-04T20:58:59.6223536Z >>>     def create_local_plan(self):
2025-03-04T20:58:59.6224147Z >>>         plan = super().create_local_plan()
2025-03-04T20:58:59.6224761Z >>>         for p in plan:
2025-03-04T20:58:59.6225313Z >>>             if p.tensor_data is not None:
2025-03-04T20:58:59.6226033Z >>>                 p.tensor_data.properties.dtype = torch.float16
2025-03-04T20:58:59.6226705Z >>>         return plan
2025-03-04T20:58:59.6227152Z >>>
2025-03-04T20:58:59.6227581Z >>>     def resolve_data(self, write_item):
2025-03-04T20:58:59.6228232Z >>>         item = super().resolve_data(write_item)
2025-03-04T20:58:59.6229206Z >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2025-03-04T20:58:59.6229989Z 
2025-03-04T20:58:59.6230603Z Using the global planning step to make central decisions that can't be made individually by each rank
2025-03-04T20:58:59.6231488Z 
2025-03-04T20:58:59.6231695Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6232333Z >>> from itertools import zip_longest
2025-03-04T20:58:59.6232935Z >>> from dataclasses import replace
2025-03-04T20:58:59.6233635Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2025-03-04T20:58:59.6234677Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
2025-03-04T20:58:59.6235682Z >>> # This sample doesn't handle ShardedTensors
2025-03-04T20:58:59.6236364Z >>>     def create_global_plan(self, all_plans):
2025-03-04T20:58:59.6237101Z >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2025-03-04T20:58:59.6237818Z >>>         items_per_rank = [
2025-03-04T20:58:59.6238417Z >>>             [item for item in items if item is not None]
2025-03-04T20:58:59.6239151Z >>>             for items in zip(*zip_longest(*iters), strict=True)
2025-03-04T20:58:59.6239657Z >>>         ]
2025-03-04T20:58:59.6240075Z >>>         all_plans = [
2025-03-04T20:58:59.6240613Z >>>             replace(plan, items=items)
2025-03-04T20:58:59.6241375Z >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2025-03-04T20:58:59.6242136Z >>>         ]
2025-03-04T20:58:59.6242646Z >>>         return super().create_global_plan(all_plans)
2025-03-04T20:58:59.6243109Z 
2025-03-04T20:58:59.6243608Z Finally, some planners need to save additional metadata in the checkpoint, this is
2025-03-04T20:58:59.6244886Z accomplished by having each rank contribute their data items in the local plan and
2025-03-04T20:58:59.6245858Z the global planner aggregate them:
2025-03-04T20:58:59.6246270Z 
2025-03-04T20:58:59.6246479Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6247303Z >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2025-03-04T20:58:59.6248030Z >>>     def create_local_plan(self) -> SavePlan:
2025-03-04T20:58:59.6248686Z >>>         plan = super().create_local_plan()
2025-03-04T20:58:59.6249405Z >>>         return replace(plan, planner_data="per-rank-data")
2025-03-04T20:58:59.6250074Z >>>
2025-03-04T20:58:59.6250865Z >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2025-03-04T20:58:59.6251997Z >>>         global_plan, metadata = super().create_global_plan(all_plans)
2025-03-04T20:58:59.6252892Z >>>         merged_data = [p.planner_data for p in global_plan]
2025-03-04T20:58:59.6253694Z >>>         metadata = replace(metadata, planner_data=merged_data)
2025-03-04T20:58:59.6254377Z >>>         return global_plan, metadata
2025-03-04T20:58:59.6254786Z 
2025-03-04T20:58:59.6255285Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6255985Z 
2025-03-04T20:58:59.6257162Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=293.
2025-03-04T20:58:59.6259226Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6259969Z 
2025-03-04T20:58:59.6260646Z Abstract class defining the protocol used by load_state_dict to plan the load process.
2025-03-04T20:58:59.6261426Z 
2025-03-04T20:58:59.6261957Z LoadPlanner are stateful objects that can be used to customize the whole load process.
2025-03-04T20:58:59.6262733Z 
2025-03-04T20:58:59.6263256Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-04T20:58:59.6264260Z will be visible to the whole process.
2025-03-04T20:58:59.6264690Z 
2025-03-04T20:58:59.6265228Z A planner subclass can expect the following sequence of calls during load_state_dict:
2025-03-04T20:58:59.6266022Z 
2025-03-04T20:58:59.6266252Z 1) set_up_planner - called on all ranks.
2025-03-04T20:58:59.6266949Z     Signals the start of loading a checkpoint.
2025-03-04T20:58:59.6267414Z 
2025-03-04T20:58:59.6267647Z 2) create_local_plan - called on all ranks.
2025-03-04T20:58:59.6268656Z     Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2025-03-04T20:58:59.6269462Z 
2025-03-04T20:58:59.6269779Z 3) create_global_plan - called on the coordinator rank only.
2025-03-04T20:58:59.6270660Z     Takes the LoadPlan from all ranks and make any global decision.
2025-03-04T20:58:59.6271220Z 
2025-03-04T20:58:59.6271402Z 4) load_bytes - called multiple times on each rank
2025-03-04T20:58:59.6271856Z     This is called once per non-tensor value in state_dict.
2025-03-04T20:58:59.6272142Z 
2025-03-04T20:58:59.6272388Z 5) resolve_tensor and commit_tensor - called multiple times on each rank
2025-03-04T20:58:59.6272933Z     They are called in pair for each Tensor value in state_dict.
2025-03-04T20:58:59.6273234Z 
2025-03-04T20:58:59.6273559Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2025-03-04T20:58:59.6274435Z most changes can be expressed by changes in a single method.
2025-03-04T20:58:59.6274746Z 
2025-03-04T20:58:59.6274901Z There are two usual patterns of extension:
2025-03-04T20:58:59.6275147Z 
2025-03-04T20:58:59.6275431Z Rewriting state_dict. This is the simplest way to extend the load process as it
2025-03-04T20:58:59.6276165Z doesn't requite understanding the intrincacies of how LoadPlan works. We need
2025-03-04T20:58:59.6277255Z to keep a reference to the original state_dict as load happens in place so
2025-03-04T20:58:59.6278135Z we need to be able to perform it in place
2025-03-04T20:58:59.6278572Z 
2025-03-04T20:58:59.6278797Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6279459Z >>> class RenamePlanner(DefaultLoadPlanner):
2025-03-04T20:58:59.6280114Z >>>     def set_up_planner(
2025-03-04T20:58:59.6280577Z >>>         self,
2025-03-04T20:58:59.6281018Z >>>         state_dict: STATE_DICT_TYPE,
2025-03-04T20:58:59.6281739Z >>>         metadata: Metadata,
2025-03-04T20:58:59.6282258Z >>>         is_coordinator: bool,
2025-03-04T20:58:59.6282804Z >>>     ) -> None:
2025-03-04T20:58:59.6283295Z >>>         self.original_state_dict = state_dict
2025-03-04T20:58:59.6284025Z >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2025-03-04T20:58:59.6284723Z >>>
2025-03-04T20:58:59.6285140Z >>>         if self.flatten_sharded_tensors:
2025-03-04T20:58:59.6285635Z >>>             state_dict = _flatten_sharded_tensors(state_dict)
2025-03-04T20:58:59.6286012Z >>>
2025-03-04T20:58:59.6286259Z >>>         if self.flatten_state_dict:
2025-03-04T20:58:59.6286682Z >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2025-03-04T20:58:59.6287089Z >>>
2025-03-04T20:58:59.6287330Z >>>         self.state_dict = state_dict
2025-03-04T20:58:59.6287673Z >>>         self.metadata = metadata
2025-03-04T20:58:59.6288023Z >>>         self.is_coordinator = is_coordinator
2025-03-04T20:58:59.6288362Z >>>
2025-03-04T20:58:59.6288604Z >>>     def load_bytes(self, read_item, value):
2025-03-04T20:58:59.6288964Z >>> # Remove the "foo_" prefix
2025-03-04T20:58:59.6289600Z >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2025-03-04T20:58:59.6290056Z 
2025-03-04T20:58:59.6290061Z 
2025-03-04T20:58:59.6290419Z Modifying resolve_tensor and commit_tensor to handle load time transformation.
2025-03-04T20:58:59.6290855Z 
2025-03-04T20:58:59.6291043Z >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:58:59.6291686Z >>> class MetaModelMaterialize(DefaultSavePlanner):
2025-03-04T20:58:59.6292404Z >>>     def resolve_tensor(self, read_item):
2025-03-04T20:58:59.6293068Z >>>         tensor = super().resolve_tensor(read_item)
2025-03-04T20:58:59.6293798Z >>>         return torch.empty_like(tensor, device="cpu")
2025-03-04T20:58:59.6294449Z >>>
2025-03-04T20:58:59.6294916Z >>>     def commit_tensor(self, read_item, tensor):
2025-03-04T20:58:59.6295668Z >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2025-03-04T20:58:59.6296156Z 
2025-03-04T20:58:59.6296623Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6297318Z 
2025-03-04T20:58:59.6418501Z 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=1106.
2025-03-04T20:58:59.6420519Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6421288Z 
2025-03-04T20:58:59.6421608Z Return the model state_dict and optimizers state_dict.
2025-03-04T20:58:59.6422167Z 
2025-03-04T20:58:59.6422598Z ``get_state_dict`` can process any module that is parallelized by PyTorch
2025-03-04T20:58:59.6423811Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
2025-03-04T20:58:59.6425025Z combination of these parallelisms. The main functions of ``get_state_dict``
2025-03-04T20:58:59.6426181Z are: 1.) returning a model and optimizer state_dict that can be resharded
2025-03-04T20:58:59.6427261Z with a different number of trainers and/or different parallelisms.
2025-03-04T20:58:59.6428396Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
2025-03-04T20:58:59.6429227Z these APIs.
2025-03-04T20:58:59.6429675Z 3.) sanity checking the result state_dict.
2025-03-04T20:58:59.6430137Z 
2025-03-04T20:58:59.6430543Z The keys of the result state dictionary are the canonical FQNs (Fully
2025-03-04T20:58:59.6431639Z Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
2025-03-04T20:58:59.6432782Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a
2025-03-04T20:58:59.6433795Z parameter is the FQN returned by ``module.named_parameters()`` or
2025-03-04T20:58:59.6434753Z ``module.named_buffers()`` when the module is not distributed by any
2025-03-04T20:58:59.6435441Z parallelisms. Since the optimizer internally uses parameter IDs to represent
2025-03-04T20:58:59.6436206Z a parameter, there will be a conversion from the parameter IDs to the
2025-03-04T20:58:59.6436679Z canonical FQNs when calling this API.
2025-03-04T20:58:59.6436910Z 
2025-03-04T20:58:59.6437152Z ``get_state_dict`` can also process a module that is not parallelized. In
2025-03-04T20:58:59.6437752Z such a case, ``get_state_dict`` only performs one function -- converting the
2025-03-04T20:58:59.6438260Z optimizer parameter IDs to the canonical FQNs.
2025-03-04T20:58:59.6438521Z 
2025-03-04T20:58:59.6438627Z Example:
2025-03-04T20:58:59.6438869Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.6439153Z     >>> import torch
2025-03-04T20:58:59.6461624Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:58:59.6462837Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-04T20:58:59.6463895Z     >>> from torch.distributed.checkpoint.state_dict import get_state_dict
2025-03-04T20:58:59.6464564Z 
2025-03-04T20:58:59.6464823Z     >>> fsdp_model = FSDP(copy.deepcopy(model))
2025-03-04T20:58:59.6465648Z     >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-04T20:58:59.6466627Z     >>> ddp_model = DDP(copy.deepcopy(model))
2025-03-04T20:58:59.6467424Z     >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-04T20:58:59.6468001Z 
2025-03-04T20:58:59.6468008Z 
2025-03-04T20:58:59.6468577Z     >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
2025-03-04T20:58:59.6469528Z     >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
2025-03-04T20:58:59.6470175Z     ...     fsdp_model, fsdp_optim
2025-03-04T20:58:59.6470643Z     ... )
2025-03-04T20:58:59.6470854Z 
2025-03-04T20:58:59.6471217Z     >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
2025-03-04T20:58:59.6472076Z     >>> # the asserts will fail.
2025-03-04T20:58:59.6472684Z     >>> assert ddp_state_dict == fsdp_state_dict
2025-03-04T20:58:59.6473408Z     >>> assert ddp_optim_state == fsdp_optim_state_dict
2025-03-04T20:58:59.6474115Z 
2025-03-04T20:58:59.6474123Z 
2025-03-04T20:58:59.6474289Z Args:
2025-03-04T20:58:59.6474775Z     model (nn.Module): the nn.Module to the model.
2025-03-04T20:58:59.6475624Z     optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
2025-03-04T20:58:59.6476511Z         The optimizers that are used to optimize ``model``.
2025-03-04T20:58:59.6477494Z     submodules (deprecated): Optional[Set[nn.Module]]: only return the model parameters
2025-03-04T20:58:59.6478339Z         that belong to the submodules.
2025-03-04T20:58:59.6478760Z     options (StateDictOptions): the options to control how
2025-03-04T20:58:59.6479285Z         model state_dict and optimizer state_dict should be returned. See
2025-03-04T20:58:59.6479763Z         `StateDictOptions` for the details.
2025-03-04T20:58:59.6480002Z 
2025-03-04T20:58:59.6480112Z Returns:
2025-03-04T20:58:59.6480459Z     ``Tuple`` that contain model state_dict and optimizer state_dict.
2025-03-04T20:58:59.6480781Z 
2025-03-04T20:58:59.6481040Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
2025-03-04T20:58:59.6481395Z 
2025-03-04T20:58:59.6481675Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6482052Z 
2025-03-04T20:58:59.6482731Z 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=62.
2025-03-04T20:58:59.6483735Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6484134Z 
2025-03-04T20:58:59.6484338Z Load a checkpoint into a distributed state dict in SPMD style.
2025-03-04T20:58:59.6484671Z 
2025-03-04T20:58:59.6485050Z Each rank must have the same keys in their ``state_dict`` provided to this
2025-03-04T20:58:59.6486076Z API. Mismatched keys may result in hangs or errors. If unsure, you can use
2025-03-04T20:58:59.6487103Z the ``utils._assert_same_keys`` API to check (but may incur communication
2025-03-04T20:58:59.6488047Z costs).
2025-03-04T20:58:59.6488284Z 
2025-03-04T20:58:59.6488634Z Each rank will try to read the least amount of data necessary
2025-03-04T20:58:59.6489653Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor`
2025-03-04T20:58:59.6490794Z or :class:`DTensor` instances, each rank only reads data for their local shards.
2025-03-04T20:58:59.6491498Z 
2025-03-04T20:58:59.6491996Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-04T20:58:59.6493215Z load will first call ``state_dict`` before attempting deserialization, followed by
2025-03-04T20:58:59.6494257Z ``load_state_dict`` once the deserialization is complete.
2025-03-04T20:58:59.6495246Z For each non-``Stateful`` object, load will deserailize the object, and then replace
2025-03-04T20:58:59.6496232Z it in the ``state_dict`` with the deserialized object.
2025-03-04T20:58:59.6496728Z 
2025-03-04T20:58:59.6496933Z .. warning::
2025-03-04T20:58:59.6497494Z     All tensors in ``state_dict`` must be allocated on their
2025-03-04T20:58:59.6498441Z     destination device *prior to* calling this function.
2025-03-04T20:58:59.6499109Z 
2025-03-04T20:58:59.6499552Z     All non-tensor data is loaded using `torch.load()` and modified in place
2025-03-04T20:58:59.6500410Z     on state_dict.
2025-03-04T20:58:59.6500674Z 
2025-03-04T20:58:59.6500842Z .. warning::
2025-03-04T20:58:59.6501637Z     Users must call `load_state_dict` on the root module to ensure load
2025-03-04T20:58:59.6502558Z     pos-processing and non-tensor data properly propagates.
2025-03-04T20:58:59.6503080Z 
2025-03-04T20:58:59.6503240Z .. note:
2025-03-04T20:58:59.6503859Z     If no process group is initialized, this function will assume the intent
2025-03-04T20:58:59.6504933Z     is to load a checkpoint into the local process. This can be useful in the
2025-03-04T20:58:59.6506058Z     case of local inference, and when using regular Tensors (as opposed to DTensor
2025-03-04T20:58:59.6506955Z      or ShardedTensor)
2025-03-04T20:58:59.6507270Z 
2025-03-04T20:58:59.6507420Z .. note:
2025-03-04T20:58:59.6507871Z     Rank 0 is assumed to be the coordinator rank.
2025-03-04T20:58:59.6508352Z 
2025-03-04T20:58:59.6508518Z Args:
2025-03-04T20:58:59.6509163Z     state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
2025-03-04T20:58:59.6510107Z     checkpoint_id (Union[str, os.PathLike, None]):
2025-03-04T20:58:59.6511004Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-04T20:58:59.6512051Z         depends on the storage. It can be a path to a folder or to a file.
2025-03-04T20:58:59.6513045Z         It can also be a key if the storage is a key-value store.
2025-03-04T20:58:59.6513701Z         (Default: ``None``)
2025-03-04T20:58:59.6514274Z     storage_reader (Optional[StorageReader]):
2025-03-04T20:58:59.6515108Z         Instance of StorageWriter used to perform reads. If this is not
2025-03-04T20:58:59.6516105Z         specified, DCP will automatically infer the reader based on the
2025-03-04T20:58:59.6517141Z         checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-04T20:58:59.6517972Z         be raised. (Default: ``None``)
2025-03-04T20:58:59.6518595Z     planner (Optional[LoadPlanner]):
2025-03-04T20:58:59.6519282Z         Instance of LoadPlanner. If this is not specificed, the default
2025-03-04T20:58:59.6520027Z         planner will be used. (Default: ``None``)
2025-03-04T20:58:59.6520706Z     process_group (Optional[ProcessGroup]):
2025-03-04T20:58:59.6521509Z         ProcessGroup to be used for cross-rank synchronization.
2025-03-04T20:58:59.6522275Z         (Default: ``None``)
2025-03-04T20:58:59.6523006Z     no_dist (bool): If ``True``, this function will assume the intent is to load
2025-03-04T20:58:59.6524132Z         a checkpoint without using cross-rank synchronization. (Default: ``False``)
2025-03-04T20:58:59.6525020Z Returns:
2025-03-04T20:58:59.6525387Z     None.
2025-03-04T20:58:59.6525618Z 
2025-03-04T20:58:59.6525781Z Examples
2025-03-04T20:58:59.6526282Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.6526778Z     >>> my_model = MyModule()
2025-03-04T20:58:59.6527359Z     >>> optimizer = Adagrad(my_model.parameters())
2025-03-04T20:58:59.6528095Z     >>> model_state_dict = my_model.state_dict()
2025-03-04T20:58:59.6528998Z     >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
2025-03-04T20:58:59.6529804Z     ...     "/checkpoint/1"
2025-03-04T20:58:59.6530242Z     ... )
2025-03-04T20:58:59.6530463Z 
2025-03-04T20:58:59.6530739Z     >>> torch.distributed.checkpoint.load_state_dict(
2025-03-04T20:58:59.6531428Z     >>>     state_dict=model_state_dict,
2025-03-04T20:58:59.6532030Z     >>>     storage_reader=fs_storage_reader,
2025-03-04T20:58:59.6532588Z     >>> )
2025-03-04T20:58:59.6532788Z 
2025-03-04T20:58:59.6533054Z     >>> # module.load_state_dict() function might have customized steps
2025-03-04T20:58:59.6533529Z     >>> # to flush the state_dict, must call it to
2025-03-04T20:58:59.6533902Z     >>> # ensure correct behavior.
2025-03-04T20:58:59.6534267Z     >>> my_model.load_state_dict(model_state_dict)
2025-03-04T20:58:59.6534514Z 
2025-03-04T20:58:59.6534709Z .. note::
2025-03-04T20:58:59.6535069Z     load_state_dict uses collectives to coordinate reads across ranks.
2025-03-04T20:58:59.6535641Z     For NCCL-based process groups, internal tensor representations of
2025-03-04T20:58:59.6536303Z     objects must be moved to the GPU device before communication takes place.
2025-03-04T20:58:59.6536893Z     In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-04T20:58:59.6537487Z     and it is the user's responsibility to ensure that this is set so that each
2025-03-04T20:58:59.6538133Z     rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-04T20:58:59.6538438Z 
2025-03-04T20:58:59.6538716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6539089Z 
2025-03-04T20:58:59.6540108Z 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=68.
2025-03-04T20:58:59.6541906Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6542634Z 
2025-03-04T20:58:59.6542859Z Save a distributed model in SPMD style.
2025-03-04T20:58:59.6543269Z 
2025-03-04T20:58:59.6543621Z This function is different from ``torch.save()`` as it handles
2025-03-04T20:58:59.6544663Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2025-03-04T20:58:59.6545398Z 
2025-03-04T20:58:59.6545896Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-04T20:58:59.6546898Z save will call ``state_dict`` before serialization.
2025-03-04T20:58:59.6547365Z 
2025-03-04T20:58:59.6547545Z .. warning::
2025-03-04T20:58:59.6548206Z     There is no guarantees of Backwards Compatibility across PyTorch versions
2025-03-04T20:58:59.6548785Z     for saved state_dicts.
2025-03-04T20:58:59.6548977Z 
2025-03-04T20:58:59.6549086Z .. warning::
2025-03-04T20:58:59.6549447Z     If using the `process_group` argument, make sure that only its ranks
2025-03-04T20:58:59.6550001Z     call `save_state_dict` and that all data in state_dict belong to it.
2025-03-04T20:58:59.6550330Z 
2025-03-04T20:58:59.6550435Z .. note::
2025-03-04T20:58:59.6550846Z     When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2025-03-04T20:58:59.6551503Z     the shard_group should be calling `save_state_dict` and the corresponding process
2025-03-04T20:58:59.6552015Z     group needs to be passed in.
2025-03-04T20:58:59.6552222Z 
2025-03-04T20:58:59.6552330Z .. note::
2025-03-04T20:58:59.6552738Z     If no process group is available, this function assumes the intention is to save the
2025-03-04T20:58:59.6553260Z      state_dict in the local process.
2025-03-04T20:58:59.6553481Z 
2025-03-04T20:58:59.6553586Z .. note:
2025-03-04T20:58:59.6553932Z     Rank 0 is assumed to be the coordinator rank.
2025-03-04T20:58:59.6554487Z 
2025-03-04T20:58:59.6554494Z 
2025-03-04T20:58:59.6554656Z Args:
2025-03-04T20:58:59.6555119Z     state_dict (Dict[str, Any]): The state_dict to save.
2025-03-04T20:58:59.6555883Z     checkpoint_id (Union[str, os.PathLike, None]):
2025-03-04T20:58:59.6556763Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-04T20:58:59.6557784Z         depends on the storage. It can be a path to a folder or to a file.
2025-03-04T20:58:59.6558697Z         It can also be a key if the storage is a key-value store.
2025-03-04T20:58:59.6559393Z         (Default: ``None``)
2025-03-04T20:58:59.6559970Z     storage_writer (Optional[StorageWriter]):
2025-03-04T20:58:59.6560848Z         Instance of StorageWriter used to perform writes. If this is not
2025-03-04T20:58:59.6561852Z         specified, DCP will automatically infer the writer based on the
2025-03-04T20:58:59.6562809Z         checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-04T20:58:59.6563623Z         be raised. (Default: ``None``)
2025-03-04T20:58:59.6564214Z     planner (Optional[SavePlanner]):
2025-03-04T20:58:59.6565127Z         Instance of SavePlanner. If this is not specificed, the default
2025-03-04T20:58:59.6565979Z         planner will be used. (Default: ``None``)
2025-03-04T20:58:59.6566668Z     process_group (Optional[ProcessGroup]):
2025-03-04T20:58:59.6567603Z         ProcessGroup to be used for cross-rank synchronization.
2025-03-04T20:58:59.6568361Z         (Default: ``None``)
2025-03-04T20:58:59.6568858Z     no_dist (bool):
2025-03-04T20:58:59.6569436Z         If ``True``, this function will assume the intent is to load
2025-03-04T20:58:59.6570305Z         a checkpoint without using cross-rank synchronization.
2025-03-04T20:58:59.6571032Z         (Default: ``False``)
2025-03-04T20:58:59.6571373Z 
2025-03-04T20:58:59.6571534Z Returns:
2025-03-04T20:58:59.6572057Z     Metadata: Metadata object for the saved checkpoint.
2025-03-04T20:58:59.6572558Z 
2025-03-04T20:58:59.6572741Z Example:
2025-03-04T20:58:59.6573145Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.6573829Z     >>> my_model = MyModule()
2025-03-04T20:58:59.6574170Z 
2025-03-04T20:58:59.6574388Z     >>> state_dict = {"model": my_model}
2025-03-04T20:58:59.6574789Z 
2025-03-04T20:58:59.6575224Z     >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-04T20:58:59.6576089Z     ...     "/checkpoint/1"
2025-03-04T20:58:59.6576567Z     ... )
2025-03-04T20:58:59.6577026Z     >>> torch.distributed.checkpoint.save(
2025-03-04T20:58:59.6577674Z     >>>     state_dict=state_dict,
2025-03-04T20:58:59.6578326Z     >>>     storage_writer=fs_storage_writer,
2025-03-04T20:58:59.6578928Z     >>> )
2025-03-04T20:58:59.6579164Z 
2025-03-04T20:58:59.6579334Z .. note::
2025-03-04T20:58:59.6579954Z     save_state_dict uses collectives to coordinate writes across ranks.
2025-03-04T20:58:59.6580995Z     For NCCL-based process groups, internal tensor representations of
2025-03-04T20:58:59.6582062Z     objects must be moved to the GPU device before communication takes place.
2025-03-04T20:58:59.6583165Z     In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-04T20:58:59.6584220Z     and it is the user's responsibility to ensure that this is set so that
2025-03-04T20:58:59.6585216Z     each rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-04T20:58:59.6585841Z 
2025-03-04T20:58:59.6586337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6587064Z 
2025-03-04T20:58:59.6588277Z 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=177.
2025-03-04T20:58:59.6590239Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6591500Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2025-03-04T20:58:59.6592806Z     staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2025-03-04T20:58:59.6593751Z 
2025-03-04T20:58:59.6593926Z     .. warning::
2025-03-04T20:58:59.6594497Z         This feature is experimental and subject to change.
2025-03-04T20:58:59.6595031Z 
2025-03-04T20:58:59.6595188Z     Args:
2025-03-04T20:58:59.6595710Z         state_dict (Dict[str, Any]): The state_dict to save.
2025-03-04T20:58:59.6596505Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-03-04T20:58:59.6597400Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-04T20:58:59.6598430Z             depends on the storage. It can be a path to a folder or to a file.
2025-03-04T20:58:59.6599424Z             It can also be a key if the storage is a key-value store.
2025-03-04T20:58:59.6600152Z             (Default: ``None``)
2025-03-04T20:58:59.6600759Z         storage_writer (Optional[StorageWriter]):
2025-03-04T20:58:59.6601627Z             Instance of StorageWriter used to perform 'stage' and  'save'. If
2025-03-04T20:58:59.6602668Z             this is not specified, DCP will automatically infer the writer based on the
2025-03-04T20:58:59.6603580Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-04T20:58:59.6604151Z             be raised. (Default: ``None``)
2025-03-04T20:58:59.6604522Z         planner (Optional[SavePlanner]):
2025-03-04T20:58:59.6605052Z             Instance of SavePlanner. If this is not specificed, the default
2025-03-04T20:58:59.6605536Z             planner will be used. (Default: ``None``)
2025-03-04T20:58:59.6605933Z         process_group (Optional[ProcessGroup]):
2025-03-04T20:58:59.6606377Z             ProcessGroup to be used for cross-rank synchronization.
2025-03-04T20:58:59.6606793Z             (Default: ``None``)
2025-03-04T20:58:59.6606994Z 
2025-03-04T20:58:59.6607087Z     Returns:
2025-03-04T20:58:59.6607452Z         Future: A future holding the resultant Metadata object from `save`.
2025-03-04T20:58:59.6607801Z 
2025-03-04T20:58:59.6607893Z     Example:
2025-03-04T20:58:59.6608142Z         >>> # xdoctest: +SKIP
2025-03-04T20:58:59.6608442Z         >>> my_model = MyModule()
2025-03-04T20:58:59.6608653Z 
2025-03-04T20:58:59.6608773Z         >>> state_dict = {"model": my_model}
2025-03-04T20:58:59.6609007Z 
2025-03-04T20:58:59.6609250Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-04T20:58:59.6609964Z         ...     "/checkpoint/1"
2025-03-04T20:58:59.6610447Z         ... )
2025-03-04T20:58:59.6611034Z         >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2025-03-04T20:58:59.6611815Z         >>>     state_dict=state_dict,
2025-03-04T20:58:59.6612421Z         >>>     storage_writer=fs_storage_writer,
2025-03-04T20:58:59.6613000Z         >>> )
2025-03-04T20:58:59.6613396Z         >>>
2025-03-04T20:58:59.6613794Z         >>> # ... do some work ...
2025-03-04T20:58:59.6614294Z         >>>
2025-03-04T20:58:59.6614733Z         >>> checkpoint_future.result()
2025-03-04T20:58:59.6615137Z 
2025-03-04T20:58:59.6615293Z     
2025-03-04T20:58:59.6615976Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6616711Z 
2025-03-04T20:58:59.6683613Z 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.
2025-03-04T20:58:59.6684738Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.6685146Z 
2025-03-04T20:58:59.6685361Z Initialize rendezvous event object and record its operations.
2025-03-04T20:58:59.6685698Z 
2025-03-04T20:58:59.6685791Z Args:
2025-03-04T20:58:59.6686061Z     run_id (str): The run id of the rendezvous.
2025-03-04T20:58:59.6686471Z     message (str): The message describing the event.
2025-03-04T20:58:59.6687004Z     node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2025-03-04T20:58:59.6687585Z     name (str): Event name. (E.g. Current action being performed).
2025-03-04T20:58:59.6688154Z     hostname (str): Hostname of the node.
2025-03-04T20:58:59.6688586Z     pid (Optional[int]): The process id of the node.
2025-03-04T20:58:59.6689103Z     master_endpoint (str): The master endpoint for the rendezvous store, if known.
2025-03-04T20:58:59.6689748Z     local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2025-03-04T20:58:59.6690317Z     rank (Optional[int]): The rank of the node, if known.
2025-03-04T20:58:59.6690686Z Returns:
2025-03-04T20:58:59.6690905Z     None
2025-03-04T20:58:59.6691125Z Example:
2025-03-04T20:58:59.6691391Z     >>> # See DynamicRendezvousHandler class
2025-03-04T20:58:59.6691735Z     >>> def _record(
2025-03-04T20:58:59.6691986Z     ...     self,
2025-03-04T20:58:59.6692237Z     ...     message: str,
2025-03-04T20:58:59.6692569Z     ...     node_state: NodeState = NodeState.RUNNING,
2025-03-04T20:58:59.6692938Z     ...     rank: Optional[int] = None,
2025-03-04T20:58:59.6693261Z     ... ) -> None:
2025-03-04T20:58:59.6693540Z     ...     construct_and_record_rdzv_event(
2025-03-04T20:58:59.6694019Z     ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2025-03-04T20:58:59.6694509Z     ...         run_id=self._settings.run_id,
2025-03-04T20:58:59.6694915Z     ...         message=message,
2025-03-04T20:58:59.6695241Z     ...         node_state=node_state,
2025-03-04T20:58:59.6695730Z     ...         hostname=self._this_node.addr,
2025-03-04T20:58:59.6696088Z     ...         pid=self._this_node.pid,
2025-03-04T20:58:59.6696511Z     ...         local_id=self._this_node.local_id,
2025-03-04T20:58:59.6696864Z     ...         rank=rank,
2025-03-04T20:58:59.6697188Z     ...     )
2025-03-04T20:58:59.6697324Z 
2025-03-04T20:58:59.6697595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.6698093Z 
2025-03-04T20:58:59.8600128Z 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.
2025-03-04T20:58:59.8601130Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.8601529Z 
2025-03-04T20:58:59.8601735Z This configures FSDP-native mixed precision training.
2025-03-04T20:58:59.8602021Z 
2025-03-04T20:58:59.8602157Z Attributes:
2025-03-04T20:58:59.8602555Z     param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2025-03-04T20:58:59.8603139Z         parameters during forward and backward and thus the dtype for
2025-03-04T20:58:59.8603703Z         forward and backward computation. Outside forward and backward, the
2025-03-04T20:58:59.8604259Z         *sharded* parameters are kept in full precision (e.g. for the
2025-03-04T20:58:59.8604796Z         optimizer step), and for model checkpointing, the parameters are
2025-03-04T20:58:59.8605299Z         always saved in full precision. (Default: ``None``)
2025-03-04T20:58:59.8605807Z     reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-04T20:58:59.8606384Z         gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2025-03-04T20:58:59.8606921Z         ``None`` but ``param_dtype`` is not ``None``, then this takes on
2025-03-04T20:58:59.8607447Z         the ``param_dtype`` value, still running gradient reduction in low
2025-03-04T20:58:59.8608107Z         precision. This is permitted to differ from ``param_dtype``, e.g.
2025-03-04T20:58:59.8608879Z         to force gradient reduction to run in full precision. (Default:
2025-03-04T20:58:59.8609523Z         ``None``)
2025-03-04T20:58:59.8609903Z     buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-04T20:58:59.8610461Z         buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2025-03-04T20:58:59.8610999Z         ``buffer_dtype`` in the first forward pass and keeps them in that
2025-03-04T20:58:59.8611547Z         dtype thereafter. For model checkpointing, the buffers are saved
2025-03-04T20:58:59.8612080Z         in full precision except for ``LOCAL_STATE_DICT``. (Default:
2025-03-04T20:58:59.8612684Z         ``None``)
2025-03-04T20:58:59.8613039Z     keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2025-03-04T20:58:59.8613607Z         gradients to full precision after the backward pass in preparation
2025-03-04T20:58:59.8614175Z         for the optimizer step. If ``True``, then FSDP keeps the gradients
2025-03-04T20:58:59.8614736Z         in the dtype used for gradient reduction, which can save memory if
2025-03-04T20:58:59.8615289Z         using a custom optimizer that supports running in low precision.
2025-03-04T20:58:59.8615741Z         (Default: ``False``)
2025-03-04T20:58:59.8616152Z     cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2025-03-04T20:58:59.8616702Z         its forward args and kwargs to ``param_dtype``. This is to ensure
2025-03-04T20:58:59.8617253Z         that parameter and input dtypes match for forward computation, as
2025-03-04T20:58:59.8617877Z         required by many ops. This may need to be set to ``True`` when only
2025-03-04T20:58:59.8618451Z         applying mixed precision to some but not all FSDP modules, in which
2025-03-04T20:58:59.8619099Z         case a mixed-precision FSDP submodule needs to recast its inputs.
2025-03-04T20:58:59.8619548Z         (Default: ``False``)
2025-03-04T20:58:59.8620068Z     cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2025-03-04T20:58:59.8620630Z         casts its forward args and kwargs to ``param_dtype``, overriding
2025-03-04T20:58:59.8621158Z         the value of ``cast_forward_inputs``. For non-root FSDP modules,
2025-03-04T20:58:59.8621632Z         this does not do anything. (Default: ``True``)
2025-03-04T20:58:59.8622130Z     _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2025-03-04T20:58:59.8622676Z         module classes to ignore for mixed precision when using an
2025-03-04T20:58:59.8623183Z         ``auto_wrap_policy``: Modules of these classes will have FSDP
2025-03-04T20:58:59.8623715Z         applied to them separately with mixed precision disabled (meaning
2025-03-04T20:58:59.8624271Z         that the final FSDP construction would deviate from the specified
2025-03-04T20:58:59.8624865Z         policy). If ``auto_wrap_policy`` is not specified, then this does
2025-03-04T20:58:59.8625395Z         not do anything. This API is experimental and subject to change.
2025-03-04T20:58:59.8625843Z         (Default: ``(_BatchNorm,)``)
2025-03-04T20:58:59.8626058Z 
2025-03-04T20:58:59.8626268Z .. note:: This API is experimental and subject to change.
2025-03-04T20:58:59.8626552Z 
2025-03-04T20:58:59.8626795Z .. note:: Only floating point tensors are cast to their specified dtypes.
2025-03-04T20:58:59.8627140Z 
2025-03-04T20:58:59.8627344Z .. note:: In ``summon_full_params``, parameters are forced to full
2025-03-04T20:58:59.8627765Z     precision, but buffers are not.
2025-03-04T20:58:59.8627997Z 
2025-03-04T20:58:59.8628213Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2025-03-04T20:58:59.8628771Z     their inputs are in a low precision like ``float16`` or ``bfloat16``.
2025-03-04T20:58:59.8629357Z     Disabling FSDP's mixed precision for those norm modules only means that
2025-03-04T20:58:59.8629947Z     the affine parameters are kept in ``float32``. However, this incurs
2025-03-04T20:58:59.8630542Z     separate all-gathers and reduce-scatters for those norm modules, which
2025-03-04T20:58:59.8631139Z     may be inefficient, so if the workload permits, the user should prefer
2025-03-04T20:58:59.8631646Z     to still apply mixed precision to those modules.
2025-03-04T20:58:59.8631926Z 
2025-03-04T20:58:59.8632144Z .. note:: By default, if the user passes a model with any ``_BatchNorm``
2025-03-04T20:58:59.8632697Z     modules and specifies an ``auto_wrap_policy``, then the batch norm
2025-03-04T20:58:59.8633265Z     modules will have FSDP applied to them separately with mixed precision
2025-03-04T20:58:59.8633809Z     disabled. See the ``_module_classes_to_ignore`` argument.
2025-03-04T20:58:59.8634164Z 
2025-03-04T20:58:59.8634383Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2025-03-04T20:58:59.8634975Z     ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2025-03-04T20:58:59.8635514Z     its ``cast_root_forward_inputs`` takes precedence over its
2025-03-04T20:58:59.8636010Z     ``cast_forward_inputs``. For non-root FSDP instances, their
2025-03-04T20:58:59.8636551Z     ``cast_root_forward_inputs`` values are ignored. The default setting is
2025-03-04T20:58:59.8637136Z     sufficient for the typical case where each FSDP instance has the same
2025-03-04T20:58:59.8637732Z     ``MixedPrecision`` configuration and only needs to cast inputs to the
2025-03-04T20:58:59.8638294Z     ``param_dtype`` at the beginning of the model's forward pass.
2025-03-04T20:58:59.8638601Z 
2025-03-04T20:58:59.8638834Z .. note:: For nested FSDP instances with different ``MixedPrecision``
2025-03-04T20:58:59.8639421Z     configurations, we recommend setting individual ``cast_forward_inputs``
2025-03-04T20:58:59.8640014Z     values to configure casting inputs or not before each instance's
2025-03-04T20:58:59.8640592Z     forward. In such a case, since the casts happen before each FSDP
2025-03-04T20:58:59.8641147Z     instance's forward, a parent FSDP instance should have its non-FSDP
2025-03-04T20:58:59.8641831Z     submodules run before its FSDP submodules to avoid the activation dtype
2025-03-04T20:58:59.8642421Z     being changed due to a different ``MixedPrecision`` configuration.
2025-03-04T20:58:59.8642756Z 
2025-03-04T20:58:59.8642870Z     Example::
2025-03-04T20:58:59.8643012Z 
2025-03-04T20:58:59.8643167Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.8643604Z         >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2025-03-04T20:58:59.8644010Z         >>> model[1] = FSDP(
2025-03-04T20:58:59.8644307Z         >>>     model[1],
2025-03-04T20:58:59.8644793Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2025-03-04T20:58:59.8645325Z         >>> )
2025-03-04T20:58:59.8645570Z         >>> model = FSDP(
2025-03-04T20:58:59.8645846Z         >>>     model,
2025-03-04T20:58:59.8646325Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2025-03-04T20:58:59.8646857Z         >>> )
2025-03-04T20:58:59.8647004Z 
2025-03-04T20:58:59.8647229Z     The above shows a working example. On the other hand, if ``model[1]``
2025-03-04T20:58:59.8647781Z     were replaced with ``model[0]``, meaning that the submodule using
2025-03-04T20:58:59.8648348Z     different ``MixedPrecision`` ran its forward first, then ``model[1]``
2025-03-04T20:58:59.8648939Z     would incorrectly see ``float16`` activations instead of ``bfloat16``
2025-03-04T20:58:59.8649384Z     ones.
2025-03-04T20:58:59.8649522Z 
2025-03-04T20:58:59.8649526Z 
2025-03-04T20:58:59.8649791Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.8650186Z 
2025-03-04T20:58:59.8650809Z 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.
2025-03-04T20:58:59.8651805Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.8652201Z 
2025-03-04T20:58:59.8652423Z ``FullStateDictConfig`` is a config class meant to be used with
2025-03-04T20:58:59.8652969Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
2025-03-04T20:58:59.8653511Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
2025-03-04T20:58:59.8654073Z dicts to save GPU memory and CPU memory, respectively. This config class
2025-03-04T20:58:59.8654633Z is meant to be used via the :func:`state_dict_type` context manager as
2025-03-04T20:58:59.8655055Z follows:
2025-03-04T20:58:59.8655195Z 
2025-03-04T20:58:59.8655328Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.8655824Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:58:59.8656364Z     >>> fsdp = FSDP(model, auto_wrap_policy=...)
2025-03-04T20:58:59.8656832Z     >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
2025-03-04T20:58:59.8657400Z     >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
2025-03-04T20:58:59.8657956Z     >>>     state = fsdp.state_dict()
2025-03-04T20:58:59.8658409Z     >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
2025-03-04T20:58:59.8659006Z     >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
2025-03-04T20:58:59.8659628Z     >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
2025-03-04T20:58:59.8660115Z     >>> if dist.get_rank() == 0:
2025-03-04T20:58:59.8660519Z     >>> # Load checkpoint only on rank 0 to avoid memory redundancy
2025-03-04T20:58:59.8660982Z     >>>     state_dict = torch.load("my_checkpoint.pt")
2025-03-04T20:58:59.8661380Z     >>>     model.load_state_dict(state_dict)
2025-03-04T20:58:59.8661868Z     >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
2025-03-04T20:58:59.8662512Z     >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
2025-03-04T20:58:59.8662987Z     >>> fsdp = FSDP(
2025-03-04T20:58:59.8663251Z     ...     model,
2025-03-04T20:58:59.8663600Z     ...     device_id=torch.cuda.current_device(),
2025-03-04T20:58:59.8663972Z     ...     auto_wrap_policy=...,
2025-03-04T20:58:59.8664297Z     ...     sync_module_states=True,
2025-03-04T20:58:59.8664613Z     ... )
2025-03-04T20:58:59.8664968Z     >>> # After this point, all ranks have FSDP model with loaded checkpoint.
2025-03-04T20:58:59.8665320Z 
2025-03-04T20:58:59.8665418Z Attributes:
2025-03-04T20:58:59.8665769Z     rank0_only (bool): If ``True``, then only rank 0 saves the full state
2025-03-04T20:58:59.8666312Z         dict, and nonzero ranks save an empty dict. If ``False``, then all
2025-03-04T20:58:59.8666809Z         ranks save the full state dict. (Default: ``False``)
2025-03-04T20:58:59.8667087Z 
2025-03-04T20:58:59.8667359Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.8667733Z 
2025-03-04T20:58:59.8744029Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=639.
2025-03-04T20:58:59.8745253Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.8745925Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-04T20:58:59.8746314Z 
2025-03-04T20:58:59.8746585Z         Also takes (optional) configuration for the model's and optimizer's state dict.
2025-03-04T20:58:59.8747450Z         The target module does not have to be a FSDP module. If the target
2025-03-04T20:58:59.8748247Z         module is a FSDP module, its ``state_dict_type`` will also be changed.
2025-03-04T20:58:59.8748265Z 
2025-03-04T20:58:59.8748668Z         .. note:: This API should be called for only the top-level (root)
2025-03-04T20:58:59.8748769Z             module.
2025-03-04T20:58:59.8748774Z 
2025-03-04T20:58:59.8749006Z         .. note:: This API enables users to transparently use the conventional
2025-03-04T20:58:59.8749213Z             ``state_dict`` API to take model checkpoints in cases where the
2025-03-04T20:58:59.8749444Z             root FSDP module is wrapped by another ``nn.Module``. For example,
2025-03-04T20:58:59.8749661Z             the following will ensure ``state_dict`` is called on all non-FSDP
2025-03-04T20:58:59.8749920Z             instances, while dispatching into `sharded_state_dict` implementation
2025-03-04T20:58:59.8750021Z             for FSDP:
2025-03-04T20:58:59.8750025Z 
2025-03-04T20:58:59.8750136Z         Example::
2025-03-04T20:58:59.8750141Z 
2025-03-04T20:58:59.8750283Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.8750411Z             >>> model = DDP(FSDP(...))
2025-03-04T20:58:59.8750668Z             >>> FSDP.set_state_dict_type(
2025-03-04T20:58:59.8750781Z             >>>     model,
2025-03-04T20:58:59.8750923Z             >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-04T20:58:59.8751162Z             >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2025-03-04T20:58:59.8751399Z             >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2025-03-04T20:58:59.8751507Z             >>> )
2025-03-04T20:58:59.8751643Z             >>> param_state_dict = model.state_dict()
2025-03-04T20:58:59.8751836Z             >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-04T20:58:59.8751841Z 
2025-03-04T20:58:59.8751935Z         Args:
2025-03-04T20:58:59.8752079Z             module (torch.nn.Module): Root module.
2025-03-04T20:58:59.8752320Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-04T20:58:59.8752580Z             state_dict_config (Optional[StateDictConfig]): the configuration for the
2025-03-04T20:58:59.8752702Z                 target ``state_dict_type``.
2025-03-04T20:58:59.8753030Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2025-03-04T20:58:59.8753154Z                 for the optimizer state dict.
2025-03-04T20:58:59.8753159Z 
2025-03-04T20:58:59.8753348Z         Returns:
2025-03-04T20:58:59.8753583Z             A StateDictSettings that include the previous state_dict type and
2025-03-04T20:58:59.8753717Z             configuration for the module.
2025-03-04T20:58:59.8753809Z         
2025-03-04T20:58:59.8754085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.8754089Z 
2025-03-04T20:58:59.8754931Z 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=797.
2025-03-04T20:58:59.8755220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.8755480Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-04T20:58:59.8755487Z 
2025-03-04T20:58:59.8755828Z         This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2025-03-04T20:58:59.8755972Z         :meth:`set_state_dict_type` for the detail.
2025-03-04T20:58:59.8755990Z 
2025-03-04T20:58:59.8756090Z         Example::
2025-03-04T20:58:59.8756095Z 
2025-03-04T20:58:59.8756250Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.8756366Z             >>> model = DDP(FSDP(...))
2025-03-04T20:58:59.8756502Z             >>> with FSDP.state_dict_type(
2025-03-04T20:58:59.8756608Z             >>>     model,
2025-03-04T20:58:59.8756747Z             >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-04T20:58:59.8756857Z             >>> ):
2025-03-04T20:58:59.8756989Z             >>>     checkpoint = model.state_dict()
2025-03-04T20:58:59.8757010Z 
2025-03-04T20:58:59.8757106Z         Args:
2025-03-04T20:58:59.8757240Z             module (torch.nn.Module): Root module.
2025-03-04T20:58:59.8757507Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-04T20:58:59.8757748Z             state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2025-03-04T20:58:59.8757936Z                 configuration for the target ``state_dict_type``.
2025-03-04T20:58:59.8758182Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2025-03-04T20:58:59.8758406Z                ``state_dict`` configuration for the target ``state_dict_type``.
2025-03-04T20:58:59.8758502Z         
2025-03-04T20:58:59.8758778Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.8758782Z 
2025-03-04T20:58:59.8801631Z 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=1810.
2025-03-04T20:58:59.8802027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.8802036Z 
2025-03-04T20:58:59.8802301Z Transform the state-dict of an optimizer corresponding to a sharded model.
2025-03-04T20:58:59.8802306Z 
2025-03-04T20:58:59.8802518Z The given state-dict can be transformed to one of three types:
2025-03-04T20:58:59.8802840Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2025-03-04T20:58:59.8802844Z 
2025-03-04T20:58:59.8803083Z For full optimizer state_dict, all states are unflattened and not sharded.
2025-03-04T20:58:59.8803337Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2025-03-04T20:58:59.8803431Z avoid OOM.
2025-03-04T20:58:59.8803436Z 
2025-03-04T20:58:59.8803689Z For sharded optimizer state_dict, all states are unflattened but sharded.
2025-03-04T20:58:59.8803900Z CPU only can be specified via :meth:`state_dict_type` to further save
2025-03-04T20:58:59.8804008Z memory.
2025-03-04T20:58:59.8804013Z 
2025-03-04T20:58:59.8804290Z For local state_dict, no transformation will be performed. But a state
2025-03-04T20:58:59.8804607Z will be converted from nn.Tensor to ShardedTensor to represent its sharding
2025-03-04T20:58:59.8804728Z nature (this is not supported yet).
2025-03-04T20:58:59.8804857Z 
2025-03-04T20:58:59.8805006Z Example::
2025-03-04T20:58:59.8805014Z 
2025-03-04T20:58:59.8805243Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.8805534Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:58:59.8805716Z     >>> from torch.distributed.fsdp import StateDictType
2025-03-04T20:58:59.8805989Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-04T20:58:59.8806376Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-04T20:58:59.8806575Z     >>> # Save a checkpoint
2025-03-04T20:58:59.8806773Z     >>> model, optim = ...
2025-03-04T20:58:59.8806979Z     >>> FSDP.set_state_dict_type(
2025-03-04T20:58:59.8807092Z     >>>     model,
2025-03-04T20:58:59.8807235Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:58:59.8807373Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8807536Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8807635Z     >>> )
2025-03-04T20:58:59.8807767Z     >>> state_dict = model.state_dict()
2025-03-04T20:58:59.8807946Z     >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-04T20:58:59.8808112Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-04T20:58:59.8808218Z     >>> # Load a checkpoint
2025-03-04T20:58:59.8808338Z     >>> model, optim = ...
2025-03-04T20:58:59.8808501Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-04T20:58:59.8808627Z     >>> FSDP.set_state_dict_type(
2025-03-04T20:58:59.8808720Z     >>>     model,
2025-03-04T20:58:59.8808862Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:58:59.8809001Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8809163Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8809257Z     >>> )
2025-03-04T20:58:59.8809392Z     >>> model.load_state_dict(state_dict)
2025-03-04T20:58:59.8809551Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-04T20:58:59.8809688Z     >>>     model, optim, optim_state_dict
2025-03-04T20:58:59.8809780Z     >>> )
2025-03-04T20:58:59.8809924Z     >>> optim.load_state_dict(optim_state_dict)
2025-03-04T20:58:59.8809929Z 
2025-03-04T20:58:59.8810018Z Args:
2025-03-04T20:58:59.8810235Z     model (torch.nn.Module): Root module (which may or may not be a
2025-03-04T20:58:59.8810447Z         :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-04T20:58:59.8810598Z         were passed into the optimizer ``optim``.
2025-03-04T20:58:59.8810788Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-04T20:58:59.8810950Z         parameters.
2025-03-04T20:58:59.8811175Z     optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2025-03-04T20:58:59.8811407Z         transform. If the value is None, optim.state_dict() will be used. (
2025-03-04T20:58:59.8811513Z         Default: ``None``)
2025-03-04T20:58:59.8811780Z     group (dist.ProcessGroup): Model's process group across which parameters
2025-03-04T20:58:59.8811975Z         are sharded or ``None`` if using the default process group. (
2025-03-04T20:58:59.8812095Z         Default: ``None``)
2025-03-04T20:58:59.8812101Z 
2025-03-04T20:58:59.8812198Z Returns:
2025-03-04T20:58:59.8812413Z     Dict[str, Any]: A :class:`dict` containing the optimizer state for
2025-03-04T20:58:59.8812590Z     ``model``. The sharding of the optimizer state is based on
2025-03-04T20:58:59.8812708Z     ``state_dict_type``.
2025-03-04T20:58:59.8812713Z 
2025-03-04T20:58:59.8812978Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.8812984Z 
2025-03-04T20:58:59.8813881Z 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=1908.
2025-03-04T20:58:59.8814228Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.8814233Z 
2025-03-04T20:58:59.8814653Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2025-03-04T20:58:59.8814658Z 
2025-03-04T20:58:59.8814854Z Given a ``optim_state_dict`` that is transformed through
2025-03-04T20:58:59.8815077Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2025-03-04T20:58:59.8815307Z state_dict that can be loaded to ``optim`` which is the optimizer for
2025-03-04T20:58:59.8815504Z ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2025-03-04T20:58:59.8815509Z 
2025-03-04T20:58:59.8815656Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.8815905Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:58:59.8816086Z     >>> from torch.distributed.fsdp import StateDictType
2025-03-04T20:58:59.8816276Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-04T20:58:59.8816502Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-04T20:58:59.8816612Z     >>> # Save a checkpoint
2025-03-04T20:58:59.8816729Z     >>> model, optim = ...
2025-03-04T20:58:59.8816899Z     >>> FSDP.set_state_dict_type(
2025-03-04T20:58:59.8817007Z     >>>     model,
2025-03-04T20:58:59.8817133Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:58:59.8817282Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8817432Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8817535Z     >>> )
2025-03-04T20:58:59.8817652Z     >>> state_dict = model.state_dict()
2025-03-04T20:58:59.8817853Z     >>> original_osd = optim.state_dict()
2025-03-04T20:58:59.8817995Z     >>> optim_state_dict = FSDP.optim_state_dict(
2025-03-04T20:58:59.8818089Z     >>>     model,
2025-03-04T20:58:59.8818201Z     >>>     optim,
2025-03-04T20:58:59.8818321Z     >>>     optim_state_dict=original_osd
2025-03-04T20:58:59.8818427Z     >>> )
2025-03-04T20:58:59.8818579Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-04T20:58:59.8818705Z     >>> # Load a checkpoint
2025-03-04T20:58:59.8818810Z     >>> model, optim = ...
2025-03-04T20:58:59.8818983Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-04T20:58:59.8819097Z     >>> FSDP.set_state_dict_type(
2025-03-04T20:58:59.8819231Z     >>>     model,
2025-03-04T20:58:59.8819377Z     >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:58:59.8819526Z     >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8819676Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:58:59.8819777Z     >>> )
2025-03-04T20:58:59.8819898Z     >>> model.load_state_dict(state_dict)
2025-03-04T20:58:59.8820103Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-04T20:58:59.8820222Z     >>>     model, optim, optim_state_dict
2025-03-04T20:58:59.8820326Z     >>> )
2025-03-04T20:58:59.8820458Z     >>> optim.load_state_dict(optim_state_dict)
2025-03-04T20:58:59.8820463Z 
2025-03-04T20:58:59.8820564Z Args:
2025-03-04T20:58:59.8820769Z     model (torch.nn.Module): Root module (which may or may not be a
2025-03-04T20:58:59.8820989Z         :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-04T20:58:59.8821129Z         were passed into the optimizer ``optim``.
2025-03-04T20:58:59.8821331Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-04T20:58:59.8821431Z         parameters.
2025-03-04T20:58:59.8821665Z     optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2025-03-04T20:58:59.8821875Z     is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2025-03-04T20:58:59.8822090Z         KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2025-03-04T20:58:59.8822277Z         KeyedOptimizer or torch.distributed's NamedOptimizer.
2025-03-04T20:58:59.8822523Z     load_directly (bool): If this is set to True, this API will also
2025-03-04T20:58:59.8822732Z         call optim.load_state_dict(result) before returning the result.
2025-03-04T20:58:59.8823045Z         Otherwise, users are responsible to call ``optim.load_state_dict()``
2025-03-04T20:58:59.8823156Z         (Default: ``False``)
2025-03-04T20:58:59.8823417Z     group (dist.ProcessGroup): Model's process group across which parameters
2025-03-04T20:58:59.8823611Z         are sharded or ``None`` if using the default process group. (
2025-03-04T20:58:59.8823729Z         Default: ``None``)
2025-03-04T20:58:59.8823733Z 
2025-03-04T20:58:59.8823998Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.8824002Z 
2025-03-04T20:58:59.9340003Z 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=128.
2025-03-04T20:58:59.9340409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9340423Z 
2025-03-04T20:58:59.9340788Z RemoteModule instance can only be created after RPC initialization.
2025-03-04T20:58:59.9340796Z 
2025-03-04T20:58:59.9341088Z It creates a user-specified module on a specified remote node.
2025-03-04T20:58:59.9341334Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-04T20:58:59.9341463Z executed on the remote node.
2025-03-04T20:58:59.9341708Z It takes care of autograd recording to ensure the backward pass propagates
2025-03-04T20:58:59.9346267Z gradients back to the corresponding remote module.
2025-03-04T20:58:59.9346933Z It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2025-03-04T20:58:59.9347272Z without incurring any overheads of copying the actual module,
2025-03-04T20:58:59.9347497Z which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2025-03-04T20:58:59.9347643Z pointing to the remote module.
2025-03-04T20:58:59.9347655Z 
2025-03-04T20:58:59.9347956Z The arguments of ``forward_async`` and ``forward`` are the same as
2025-03-04T20:58:59.9348235Z the ``forward`` method of the module returned by the ``module_cls``.
2025-03-04T20:58:59.9348251Z 
2025-03-04T20:58:59.9348637Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2025-03-04T20:58:59.9348642Z 
2025-03-04T20:58:59.9348922Z Particularly, to create a hybrid model, typically the local modules should be
2025-03-04T20:58:59.9349407Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2025-03-04T20:58:59.9349512Z Hybrid Example:
2025-03-04T20:58:59.9349650Z         >>> class HybridModel(nn.Module):
2025-03-04T20:58:59.9349771Z         >>>     def __init__(self) -> None:
2025-03-04T20:58:59.9350013Z         >>>         nn.Module.__init__(self)
2025-03-04T20:58:59.9350216Z         >>>         self.remote_embedding = RemoteModule(...)
2025-03-04T20:58:59.9350365Z         >>>         self.local_linear = nn.Linear(...)
2025-03-04T20:58:59.9350371Z 
2025-03-04T20:58:59.9350579Z For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-04T20:58:59.9350905Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2025-03-04T20:58:59.9351124Z the generated ``RemoteModule`` will have 2 methods in signature of
2025-03-04T20:58:59.9351278Z ``def forward(input: Tensor) -> Tensor:`` and
2025-03-04T20:58:59.9351449Z ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2025-03-04T20:58:59.9351454Z 
2025-03-04T20:58:59.9351608Z .. note::
2025-03-04T20:58:59.9351790Z     If the remote module is placed on a cuda device,
2025-03-04T20:58:59.9352048Z     any input CPU tensors will be automatically moved to the same cuda device,
2025-03-04T20:58:59.9352518Z     and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2025-03-04T20:58:59.9352587Z 
2025-03-04T20:58:59.9352692Z Args:
2025-03-04T20:58:59.9352997Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-04T20:58:59.9353425Z         The device can be a local device or a remote device specified by one of the following remote
2025-03-04T20:58:59.9353524Z         formats:
2025-03-04T20:58:59.9353529Z 
2025-03-04T20:58:59.9353691Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-04T20:58:59.9353861Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-04T20:58:59.9353867Z 
2025-03-04T20:58:59.9354181Z         In addition, the device field can be optional and the default value is "cpu".
2025-03-04T20:58:59.9354308Z     module_cls (nn.Module): For example,
2025-03-04T20:58:59.9354440Z         >>> class MyModule(nn.Module):
2025-03-04T20:58:59.9354549Z         >>>     def forward(input):
2025-03-04T20:58:59.9354706Z         >>>         return input + 1
2025-03-04T20:58:59.9354815Z         >>>
2025-03-04T20:58:59.9354940Z         >>> module_cls = MyModule
2025-03-04T20:58:59.9355153Z     args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-04T20:58:59.9355366Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-04T20:58:59.9355717Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-04T20:58:59.9355976Z         to be created. The type object should be decorated by @torch.jit.interface.
2025-03-04T20:58:59.9356235Z         If not provided, the generated RemoteModule is not torchscript-able.
2025-03-04T20:58:59.9356584Z         Warning, this is an experimental API and susceptible to frequent changes.
2025-03-04T20:58:59.9356590Z 
2025-03-04T20:58:59.9356688Z Returns:
2025-03-04T20:58:59.9356969Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-04T20:58:59.9357312Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-04T20:58:59.9357645Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-04T20:58:59.9357877Z     on the user-provided module on the remote side.
2025-03-04T20:58:59.9357886Z 
2025-03-04T20:58:59.9358045Z Example::
2025-03-04T20:58:59.9358253Z     Run the following code in two different processes:
2025-03-04T20:58:59.9358276Z 
2025-03-04T20:58:59.9358435Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:58:59.9358538Z     >>> # On worker 0:
2025-03-04T20:58:59.9358653Z     >>> import torch
2025-03-04T20:58:59.9358789Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9358919Z     >>> from torch import nn, Tensor
2025-03-04T20:58:59.9359159Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-04T20:58:59.9359264Z     >>>
2025-03-04T20:58:59.9359411Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:58:59.9359606Z     >>> remote_linear_module = RemoteModule(
2025-03-04T20:58:59.9359742Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-04T20:58:59.9359853Z     >>> )
2025-03-04T20:58:59.9359966Z     >>> input = torch.randn(128, 20)
2025-03-04T20:58:59.9360145Z     >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-04T20:58:59.9360254Z     >>> ret = ret_fut.wait()
2025-03-04T20:58:59.9360375Z     >>> rpc.shutdown()
2025-03-04T20:58:59.9360380Z 
2025-03-04T20:58:59.9360479Z     >>> # On worker 1:
2025-03-04T20:58:59.9360592Z     >>> import torch
2025-03-04T20:58:59.9360722Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9360825Z     >>>
2025-03-04T20:58:59.9360975Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:58:59.9361089Z     >>> rpc.shutdown()
2025-03-04T20:58:59.9361094Z 
2025-03-04T20:58:59.9361357Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9361362Z 
2025-03-04T20:58:59.9362140Z 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=505.
2025-03-04T20:58:59.9362462Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9362467Z 
2025-03-04T20:58:59.9362830Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2025-03-04T20:58:59.9362835Z 
2025-03-04T20:58:59.9363179Z This alternate initialization method can be particularly useful if we want to create multiple
2025-03-04T20:58:59.9363502Z RemoteModule instances that share the same underlying module and reduce memory consumption.
2025-03-04T20:58:59.9363522Z 
2025-03-04T20:58:59.9363810Z Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2025-03-04T20:58:59.9364010Z which is not supported. The recommended way is as follows:
2025-03-04T20:58:59.9364015Z 
2025-03-04T20:58:59.9364140Z     1. the sender creates a RemoteModule;
2025-03-04T20:58:59.9364305Z     2. the sender sends its ``module_rref`` over RPC;
2025-03-04T20:58:59.9364650Z     3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2025-03-04T20:58:59.9364657Z 
2025-03-04T20:58:59.9364770Z Example::
2025-03-04T20:58:59.9364930Z     Run the following code in two different processes:
2025-03-04T20:58:59.9364936Z 
2025-03-04T20:58:59.9365069Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:58:59.9365167Z     >>> # On worker 0:
2025-03-04T20:58:59.9365276Z     >>> import torch
2025-03-04T20:58:59.9365430Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9365583Z     >>> from torch import nn, Tensor
2025-03-04T20:58:59.9365888Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-04T20:58:59.9365993Z     >>>
2025-03-04T20:58:59.9366139Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:58:59.9366272Z     >>> remote_module = RemoteModule(
2025-03-04T20:58:59.9366408Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-04T20:58:59.9366500Z     >>> )
2025-03-04T20:58:59.9366603Z     >>>
2025-03-04T20:58:59.9366723Z     >>> remote_module1 = rpc.rpc_sync(
2025-03-04T20:58:59.9366844Z     >>>     "worker1/cpu",
2025-03-04T20:58:59.9366977Z     >>>     RemoteModule.init_from_module_rref,
2025-03-04T20:58:59.9367154Z     >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2025-03-04T20:58:59.9367244Z     >>> )
2025-03-04T20:58:59.9367359Z     >>> rpc.shutdown()
2025-03-04T20:58:59.9367364Z 
2025-03-04T20:58:59.9367464Z     >>> # On worker 1:
2025-03-04T20:58:59.9367574Z     >>> import torch
2025-03-04T20:58:59.9367708Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9367811Z     >>>
2025-03-04T20:58:59.9367961Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:58:59.9368074Z     >>> rpc.shutdown()
2025-03-04T20:58:59.9368078Z 
2025-03-04T20:58:59.9368168Z Args:
2025-03-04T20:58:59.9368489Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-04T20:58:59.9368820Z         The device can be a local device or a remote device specified by one of the following remote
2025-03-04T20:58:59.9368932Z         formats:
2025-03-04T20:58:59.9368937Z 
2025-03-04T20:58:59.9369085Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-04T20:58:59.9369260Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-04T20:58:59.9369265Z 
2025-03-04T20:58:59.9369516Z         In addition, the device field can be optional and the default value is "cpu".
2025-03-04T20:58:59.9369787Z     module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2025-03-04T20:58:59.9369902Z         the created remote module.
2025-03-04T20:58:59.9370202Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-04T20:58:59.9370444Z         to be created. The type object should be decorated by @torch.jit.interface.
2025-03-04T20:58:59.9370686Z         If not provided, the generated RemoteModule is not torchscript-able.
2025-03-04T20:58:59.9370928Z         Warning, this is an experimental API and susceptible to frequent changes.
2025-03-04T20:58:59.9370962Z 
2025-03-04T20:58:59.9371069Z Returns:
2025-03-04T20:58:59.9371319Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-04T20:58:59.9371606Z     user-provided ``module_rref``, it has a blocking ``forward`` method and an
2025-03-04T20:58:59.9371889Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-04T20:58:59.9372055Z     on the user-provided module on the remote side.
2025-03-04T20:58:59.9372060Z 
2025-03-04T20:58:59.9372325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9372348Z 
2025-03-04T20:58:59.9373024Z 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=597.
2025-03-04T20:58:59.9373316Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9373323Z 
2025-03-04T20:58:59.9373562Z     A RemoteModule instance can only be created after RPC initialization.
2025-03-04T20:58:59.9373567Z 
2025-03-04T20:58:59.9374013Z     It creates a user-specified module on a specified remote node.
2025-03-04T20:58:59.9374263Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-04T20:58:59.9374396Z     executed on the remote node.
2025-03-04T20:58:59.9374642Z     It takes care of autograd recording to ensure the backward pass propagates
2025-03-04T20:58:59.9374827Z     gradients back to the corresponding remote module.
2025-03-04T20:58:59.9374832Z 
2025-03-04T20:58:59.9375142Z     It generates two methods ``forward_async`` and ``forward`` based on the
2025-03-04T20:58:59.9375385Z     signature of the ``forward`` method of ``module_cls``. ``forward_async``
2025-03-04T20:58:59.9375645Z     runs asynchronously and returns a Future. The arguments of ``forward_async``
2025-03-04T20:58:59.9375868Z     and ``forward`` are the same as the ``forward`` method of the module
2025-03-04T20:58:59.9375990Z     returned by the ``module_cls``.
2025-03-04T20:58:59.9375994Z 
2025-03-04T20:58:59.9376218Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-04T20:58:59.9376481Z     that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2025-03-04T20:58:59.9376734Z     the generated ``RemoteModule`` will have 2 methods with the signatures:
2025-03-04T20:58:59.9376739Z 
2025-03-04T20:58:59.9376874Z     | ``def forward(input: Tensor) -> Tensor:``
2025-03-04T20:58:59.9377061Z     | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2025-03-04T20:58:59.9377068Z 
2025-03-04T20:58:59.9377160Z Args:
2025-03-04T20:58:59.9377476Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-04T20:58:59.9377895Z         The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2025-03-04T20:58:59.9378110Z         E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2025-03-04T20:58:59.9378367Z         In addition, the device field can be optional and the default value is "cpu".
2025-03-04T20:58:59.9378641Z     module_cls (nn.Module): Class for the module to be created remotely. For example,
2025-03-04T20:58:59.9378645Z 
2025-03-04T20:58:59.9378765Z         >>> class MyModule(nn.Module):
2025-03-04T20:58:59.9378885Z         >>>     def forward(input):
2025-03-04T20:58:59.9378994Z         >>>         return input + 1
2025-03-04T20:58:59.9379098Z         >>>
2025-03-04T20:58:59.9379207Z         >>> module_cls = MyModule
2025-03-04T20:58:59.9379211Z 
2025-03-04T20:58:59.9379437Z     args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-04T20:58:59.9379637Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-04T20:58:59.9379642Z 
2025-03-04T20:58:59.9379751Z Returns:
2025-03-04T20:58:59.9380004Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-04T20:58:59.9380297Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-04T20:58:59.9380575Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-04T20:58:59.9380783Z     on the user-provided module on the remote side.
2025-03-04T20:58:59.9380788Z 
2025-03-04T20:58:59.9380891Z Example::
2025-03-04T20:58:59.9381062Z     Run the following code in two different processes:
2025-03-04T20:58:59.9381067Z 
2025-03-04T20:58:59.9381195Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:58:59.9381345Z     >>> # On worker 0:
2025-03-04T20:58:59.9381445Z     >>> import torch
2025-03-04T20:58:59.9381594Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9381708Z     >>> from torch import nn, Tensor
2025-03-04T20:58:59.9381948Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-04T20:58:59.9382041Z     >>>
2025-03-04T20:58:59.9382198Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:58:59.9382330Z     >>> remote_linear_module = RemoteModule(
2025-03-04T20:58:59.9382473Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-04T20:58:59.9382566Z     >>> )
2025-03-04T20:58:59.9382687Z     >>> input = torch.randn(128, 20)
2025-03-04T20:58:59.9382850Z     >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-04T20:58:59.9382968Z     >>> ret = ret_fut.wait()
2025-03-04T20:58:59.9383070Z     >>> rpc.shutdown()
2025-03-04T20:58:59.9383075Z 
2025-03-04T20:58:59.9383184Z     >>> # On worker 1:
2025-03-04T20:58:59.9383281Z     >>> import torch
2025-03-04T20:58:59.9383424Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9383554Z     >>>
2025-03-04T20:58:59.9383714Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:58:59.9383813Z     >>> rpc.shutdown()
2025-03-04T20:58:59.9383818Z 
2025-03-04T20:58:59.9384034Z     Furthermore, a more practical example that is combined with
2025-03-04T20:58:59.9384535Z     `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2025-03-04T20:58:59.9384890Z     can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2025-03-04T20:58:59.9384895Z 
2025-03-04T20:58:59.9385159Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9385164Z 
2025-03-04T20:58:59.9585485Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130.
2025-03-04T20:58:59.9585775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9585781Z 
2025-03-04T20:58:59.9586047Z DistributedOptimizer takes remote references to parameters scattered
2025-03-04T20:58:59.9586296Z across workers and applies the given optimizer locally for each parameter.
2025-03-04T20:58:59.9586415Z 
2025-03-04T20:58:59.9586676Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2025-03-04T20:58:59.9586842Z to retrieve the gradients for specific parameters.
2025-03-04T20:58:59.9586860Z 
2025-03-04T20:58:59.9586964Z Concurrent calls to
2025-03-04T20:58:59.9587182Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2025-03-04T20:58:59.9587346Z either from the same or different clients, will
2025-03-04T20:58:59.9587579Z be serialized on each worker -- as each worker's optimizer can only work
2025-03-04T20:58:59.9587806Z on one set of gradients at a time. However, there is no guarantee that
2025-03-04T20:58:59.9588060Z the full forward-backward-optimizer sequence will execute for one client
2025-03-04T20:58:59.9588317Z at a time. This means that the gradients being applied may not correspond
2025-03-04T20:58:59.9588561Z to the latest forward pass executed on a given worker. Also, there is no
2025-03-04T20:58:59.9588685Z guaranteed ordering across workers.
2025-03-04T20:58:59.9588692Z 
2025-03-04T20:58:59.9588979Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2025-03-04T20:58:59.9589264Z by default, so that optimizer updates are not blocked by the Python Global
2025-03-04T20:58:59.9589529Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2025-03-04T20:58:59.9589825Z Model Parallel). This feature is currently enabled for most optimizers. You
2025-03-04T20:58:59.9590101Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2025-03-04T20:58:59.9590218Z for your own custom optimizers.
2025-03-04T20:58:59.9590222Z 
2025-03-04T20:58:59.9590328Z Args:
2025-03-04T20:58:59.9590539Z     optimizer_class (optim.Optimizer): the class of optimizer to
2025-03-04T20:58:59.9590667Z         instantiate on each worker.
2025-03-04T20:58:59.9590887Z     params_rref (list[RRef]): list of RRefs to local or remote parameters
2025-03-04T20:58:59.9591000Z         to optimize.
2025-03-04T20:58:59.9591224Z     args: arguments to pass to the optimizer constructor on each worker.
2025-03-04T20:58:59.9591469Z     kwargs: arguments to pass to the optimizer constructor on each worker.
2025-03-04T20:58:59.9591476Z 
2025-03-04T20:58:59.9591584Z Example::
2025-03-04T20:58:59.9591746Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:58:59.9591947Z     >>> import torch.distributed.autograd as dist_autograd
2025-03-04T20:58:59.9592098Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:58:59.9592211Z     >>> from torch import optim
2025-03-04T20:58:59.9592424Z     >>> from torch.distributed.optim import DistributedOptimizer
2025-03-04T20:58:59.9592515Z     >>>
2025-03-04T20:58:59.9592673Z     >>> with dist_autograd.context() as context_id:
2025-03-04T20:58:59.9592837Z     >>>   # Forward pass.
2025-03-04T20:58:59.9593061Z     >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2025-03-04T20:58:59.9593264Z     >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2025-03-04T20:58:59.9593411Z     >>>   loss = rref1.to_here() + rref2.to_here()
2025-03-04T20:58:59.9593502Z     >>>
2025-03-04T20:58:59.9593621Z     >>>   # Backward pass.
2025-03-04T20:58:59.9593782Z     >>>   dist_autograd.backward(context_id, [loss.sum()])
2025-03-04T20:58:59.9593873Z     >>>
2025-03-04T20:58:59.9593986Z     >>>   # Optimizer.
2025-03-04T20:58:59.9594121Z     >>>   dist_optim = DistributedOptimizer(
2025-03-04T20:58:59.9594235Z     >>>      optim.SGD,
2025-03-04T20:58:59.9594339Z     >>>      [rref1, rref2],
2025-03-04T20:58:59.9594445Z     >>>      lr=0.05,
2025-03-04T20:58:59.9594536Z     >>>   )
2025-03-04T20:58:59.9594667Z     >>>   dist_optim.step(context_id)
2025-03-04T20:58:59.9594672Z 
2025-03-04T20:58:59.9594838Z __ https://github.com/pytorch/tutorials/pull/1465
2025-03-04T20:58:59.9594843Z 
2025-03-04T20:58:59.9595117Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9595121Z 
2025-03-04T20:58:59.9604182Z 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.
2025-03-04T20:58:59.9604526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9604532Z 
2025-03-04T20:58:59.9604946Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-04T20:58:59.9605106Z This optimizer runs local optimizer at every step.
2025-03-04T20:58:59.9605459Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
2025-03-04T20:58:59.9605464Z 
2025-03-04T20:58:59.9605558Z Args:
2025-03-04T20:58:59.9605684Z     optim: The local optimizer.
2025-03-04T20:58:59.9605915Z     averager: A model averager instance to run post-localSGD algorithm.
2025-03-04T20:58:59.9605920Z 
2025-03-04T20:58:59.9606039Z Example::
2025-03-04T20:58:59.9606044Z 
2025-03-04T20:58:59.9606178Z     >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:58:59.9606293Z     >>> import torch
2025-03-04T20:58:59.9606417Z     >>> import torch.distributed as dist
2025-03-04T20:58:59.9606742Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-04T20:58:59.9606852Z     >>> import torch.nn as nn
2025-03-04T20:58:59.9607115Z     >>> from torch.distributed.optim import PostLocalSGDOptimizer
2025-03-04T20:58:59.9607395Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-04T20:58:59.9607524Z     >>>   PostLocalSGDState,
2025-03-04T20:58:59.9607636Z     >>>   post_localSGD_hook,
2025-03-04T20:58:59.9607754Z     >>> )
2025-03-04T20:58:59.9607844Z     >>>
2025-03-04T20:58:59.9608022Z     >>> model = nn.parallel.DistributedDataParallel(
2025-03-04T20:58:59.9608173Z     >>>    module, device_ids=[rank], output_device=rank
2025-03-04T20:58:59.9608277Z     >>> )
2025-03-04T20:58:59.9608367Z     >>>
2025-03-04T20:58:59.9608533Z     >>> # Register a post-localSGD communication hook.
2025-03-04T20:58:59.9608843Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-04T20:58:59.9609027Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:58:59.9609118Z     >>>
2025-03-04T20:58:59.9609343Z     >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2025-03-04T20:58:59.9609602Z     >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2025-03-04T20:58:59.9609790Z     >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-04T20:58:59.9610011Z     >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2025-03-04T20:58:59.9610162Z     >>> opt = PostLocalSGDOptimizer(
2025-03-04T20:58:59.9610310Z     >>>     optim=local_optim,
2025-03-04T20:58:59.9610582Z     >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-04T20:58:59.9610674Z     >>> )
2025-03-04T20:58:59.9610779Z     >>>
2025-03-04T20:58:59.9611066Z     >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2025-03-04T20:58:59.9611392Z     >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2025-03-04T20:58:59.9611780Z     >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2025-03-04T20:58:59.9611910Z     >>> for step in range(0, 200):
2025-03-04T20:58:59.9612016Z     >>>    opt.zero_grad()
2025-03-04T20:58:59.9612151Z     >>>    loss = loss_fn(output, labels)
2025-03-04T20:58:59.9612256Z     >>>    loss.backward()
2025-03-04T20:58:59.9612371Z     >>>    opt.step()
2025-03-04T20:58:59.9612375Z 
2025-03-04T20:58:59.9612641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9612646Z 
2025-03-04T20:58:59.9718504Z 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.
2025-03-04T20:58:59.9718903Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9718912Z 
2025-03-04T20:58:59.9719344Z Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2025-03-04T20:58:59.9719349Z 
2025-03-04T20:58:59.9719486Z The sharing is done as described by ZeRO_.
2025-03-04T20:58:59.9719491Z 
2025-03-04T20:58:59.9719657Z The local optimizer instance in each rank is only
2025-03-04T20:58:59.9719908Z responsible for updating approximately ``1 / world_size`` parameters and
2025-03-04T20:58:59.9720130Z hence only needs to keep ``1 / world_size`` optimizer states. After
2025-03-04T20:58:59.9720386Z parameters are updated locally, each rank will broadcast its parameters to
2025-03-04T20:58:59.9720592Z all other peers to keep all model replicas in the same state.
2025-03-04T20:58:59.9720799Z ``ZeroRedundancyOptimizer`` can be used in conjunction with
2025-03-04T20:58:59.9721083Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2025-03-04T20:58:59.9721190Z memory consumption.
2025-03-04T20:58:59.9721245Z 
2025-03-04T20:58:59.9721527Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2025-03-04T20:58:59.9721768Z of parameters at each rank. Each parameter belongs to a single rank and is
2025-03-04T20:58:59.9722073Z not divided among ranks. The partition is arbitrary and might not match the
2025-03-04T20:58:59.9722215Z the parameter registration or usage order.
2025-03-04T20:58:59.9722220Z 
2025-03-04T20:58:59.9722331Z Arguments:
2025-03-04T20:58:59.9722534Z     params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2025-03-04T20:58:59.9722745Z         or :class:`dict` s giving all parameters, which will be sharded
2025-03-04T20:58:59.9722885Z         across ranks.
2025-03-04T20:58:59.9722890Z 
2025-03-04T20:58:59.9722987Z Keyword Args:
2025-03-04T20:58:59.9723233Z     optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2025-03-04T20:58:59.9723334Z         optimizer.
2025-03-04T20:58:59.9723565Z     process_group (``ProcessGroup``, optional): ``torch.distributed``
2025-03-04T20:58:59.9723777Z         ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2025-03-04T20:58:59.9723947Z         :meth:`torch.distributed.init_process_group`).
2025-03-04T20:58:59.9724186Z     parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2025-03-04T20:58:59.9724419Z         packed into buckets to speed up communication, and ``param.data``
2025-03-04T20:58:59.9724626Z         fields point to bucket views at different offsets; if ``False``,
2025-03-04T20:58:59.9724851Z         each individual parameter is communicated separately, and each
2025-03-04T20:58:59.9725060Z         ``params.data`` stays intact (default: ``False``).
2025-03-04T20:58:59.9725275Z     overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2025-03-04T20:58:59.9725480Z         overlapped with :class:`DistributedDataParallel` 's gradient
2025-03-04T20:58:59.9725814Z         synchronization; this requires (1) either a functional optimizer
2025-03-04T20:58:59.9726103Z         for the ``optimizer_class`` argument or one with a functional
2025-03-04T20:58:59.9726302Z         equivalent and (2) registering a DDP communication hook
2025-03-04T20:58:59.9726516Z         constructed from one of the functions in ``ddp_zero_hook.py``;
2025-03-04T20:58:59.9726703Z         parameters are packed into buckets matching those in
2025-03-04T20:58:59.9726868Z         :class:`DistributedDataParallel`, meaning that the
2025-03-04T20:58:59.9727036Z         ``parameters_as_bucket_view`` argument is ignored.
2025-03-04T20:58:59.9727234Z         If ``False``, :meth:`step` runs disjointly after the backward pass
2025-03-04T20:58:59.9727348Z         (per normal).
2025-03-04T20:58:59.9727455Z         (default: ``False``)
2025-03-04T20:58:59.9727686Z     **defaults: any trailing arguments, which are forwarded to the local
2025-03-04T20:58:59.9727823Z         optimizer.
2025-03-04T20:58:59.9727828Z 
2025-03-04T20:58:59.9727946Z Example::
2025-03-04T20:58:59.9727951Z 
2025-03-04T20:58:59.9728059Z     >>> # xdoctest: +SKIP
2025-03-04T20:58:59.9728183Z     >>> import torch.nn as nn
2025-03-04T20:58:59.9728397Z     >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2025-03-04T20:58:59.9728619Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-04T20:58:59.9728854Z     >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2025-03-04T20:58:59.9728989Z     >>> ddp = DDP(model, device_ids=[rank])
2025-03-04T20:58:59.9729113Z     >>> opt = ZeroRedundancyOptimizer(
2025-03-04T20:58:59.9729250Z     >>>     ddp.parameters(),
2025-03-04T20:58:59.9729383Z     >>>     optimizer_class=torch.optim.Adam,
2025-03-04T20:58:59.9729492Z     >>>     lr=0.01
2025-03-04T20:58:59.9729585Z     >>> )
2025-03-04T20:58:59.9729716Z     >>> ddp(inputs).sum().backward()
2025-03-04T20:58:59.9729814Z     >>> opt.step()
2025-03-04T20:58:59.9729821Z 
2025-03-04T20:58:59.9729932Z .. warning::
2025-03-04T20:58:59.9730152Z     Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2025-03-04T20:58:59.9730351Z     passed-in parameters are the same dense type.
2025-03-04T20:58:59.9730355Z 
2025-03-04T20:58:59.9730455Z .. warning::
2025-03-04T20:58:59.9730719Z     If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2025-03-04T20:58:59.9730932Z     the way that overlapping :class:`DistributedDataParallel` with
2025-03-04T20:58:59.9731189Z     :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2025-03-04T20:58:59.9731414Z     two or three training iterations do not perform parameter updates in
2025-03-04T20:58:59.9731633Z     the optimizer step, depending on if ``static_graph=False`` or
2025-03-04T20:58:59.9731828Z     ``static_graph=True``, respectively. This is because it needs
2025-03-04T20:58:59.9732038Z     information about the gradient bucketing strategy used by
2025-03-04T20:58:59.9732270Z     :class:`DistributedDataParallel`, which is not finalized until the
2025-03-04T20:58:59.9732497Z     second forward pass if ``static_graph=False`` or until the third
2025-03-04T20:58:59.9732715Z     forward pass if ``static_graph=True``. To adjust for this, one option
2025-03-04T20:58:59.9732848Z     is to prepend dummy inputs.
2025-03-04T20:58:59.9732852Z 
2025-03-04T20:58:59.9733120Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2025-03-04T20:58:59.9733124Z 
2025-03-04T20:58:59.9733280Z .. _ZeRO: https://arxiv.org/abs/1910.02054
2025-03-04T20:58:59.9733284Z 
2025-03-04T20:58:59.9733288Z 
2025-03-04T20:58:59.9733550Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9733554Z 
2025-03-04T20:58:59.9933419Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28.
2025-03-04T20:58:59.9934000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:58:59.9934020Z 
2025-03-04T20:58:59.9934445Z Custom reducer class that can be used to specify a custom operation that
2025-03-04T20:58:59.9934779Z reduces losses of multiple microbatches into one value.
2025-03-04T20:58:59.9934789Z 
2025-03-04T20:58:59.9934948Z Example:
2025-03-04T20:58:59.9935124Z >>> # xdoctest: +SKIP
2025-03-04T20:58:59.9935311Z >>> sum_reducer = _CustomReducer(
2025-03-04T20:58:59.9935505Z >>>     torch.tensor(0.0),
2025-03-04T20:58:59.9935688Z >>>     lambda a, b: a + b
2025-03-04T20:58:59.9935861Z >>> )
2025-03-04T20:58:59.9935871Z 
2025-03-04T20:58:59.9936343Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:58:59.9936352Z 
2025-03-04T20:59:00.0414967Z 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.
2025-03-04T20:59:00.0416233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.0416738Z 
2025-03-04T20:59:00.0417009Z A decorator for a function indicating that the return value of the function
2025-03-04T20:59:00.0417615Z is guaranteed to be a :class:`~torch.futures.Future` object and this
2025-03-04T20:59:00.0418283Z function can run asynchronously on the RPC callee. More specifically, the
2025-03-04T20:59:00.0418921Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2025-03-04T20:59:00.0419576Z function and installs subsequent processing steps as a callback to that
2025-03-04T20:59:00.0420184Z :class:`~torch.futures.Future`. The installed callback will read the value
2025-03-04T20:59:00.0420853Z from the :class:`~torch.futures.Future` when completed and send the
2025-03-04T20:59:00.0421506Z value back as the RPC response. That also means the returned
2025-03-04T20:59:00.0422141Z :class:`~torch.futures.Future` only exists on the callee side and is never
2025-03-04T20:59:00.0422739Z sent through RPC. This decorator is useful when the wrapped function's
2025-03-04T20:59:00.0423314Z (``fn``) execution needs to pause and resume due to, e.g., containing
2025-03-04T20:59:00.0423977Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2025-03-04T20:59:00.0424330Z 
2025-03-04T20:59:00.0424588Z .. note:: To enable asynchronous execution, applications must pass the
2025-03-04T20:59:00.0425228Z     function object returned by this decorator to RPC APIs. If RPC detected
2025-03-04T20:59:00.0425830Z     attributes installed by this decorator, it knows that this function
2025-03-04T20:59:00.0426377Z     returns a ``Future`` object and will handle that accordingly.
2025-03-04T20:59:00.0426919Z     However, this does not mean this decorator has to be outmost one when
2025-03-04T20:59:00.0427508Z     defining a function. For example, when combined with ``@staticmethod``
2025-03-04T20:59:00.0428092Z     or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2025-03-04T20:59:00.0428668Z     inner decorator to allow the target function be recognized as a static
2025-03-04T20:59:00.0429260Z     or class function. This target function can still execute asynchronously
2025-03-04T20:59:00.0429858Z     because, when accessed, the static or class method preserves attributes
2025-03-04T20:59:00.0430383Z     installed by ``@rpc.functions.async_execution``.
2025-03-04T20:59:00.0430656Z 
2025-03-04T20:59:00.0430662Z 
2025-03-04T20:59:00.0430774Z Example::
2025-03-04T20:59:00.0431121Z     The returned :class:`~torch.futures.Future` object can come from
2025-03-04T20:59:00.0431582Z     :meth:`~torch.distributed.rpc.rpc_async`,
2025-03-04T20:59:00.0432072Z     :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2025-03-04T20:59:00.0432746Z     constructor. The example below shows directly using the
2025-03-04T20:59:00.0433275Z     :class:`~torch.futures.Future` returned by
2025-03-04T20:59:00.0433716Z     :meth:`~torch.futures.Future.then`.
2025-03-04T20:59:00.0433980Z 
2025-03-04T20:59:00.0434109Z     >>> from torch.distributed import rpc
2025-03-04T20:59:00.0434486Z     >>>
2025-03-04T20:59:00.0434739Z     >>> # omitting setup and shutdown RPC
2025-03-04T20:59:00.0435071Z     >>>
2025-03-04T20:59:00.0435304Z     >>> # On all workers
2025-03-04T20:59:00.0435604Z     >>> @rpc.functions.async_execution
2025-03-04T20:59:00.0435960Z     >>> def async_add_chained(to, x, y, z):
2025-03-04T20:59:00.0436402Z     >>>     # This function runs on "worker1" and returns immediately when
2025-03-04T20:59:00.0436926Z     >>>     # the callback is installed through the `then(cb)` API. In the
2025-03-04T20:59:00.0437444Z     >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2025-03-04T20:59:00.0437927Z     >>>     # When the return value of that `rpc_async` arrives at
2025-03-04T20:59:00.0438524Z     >>>     # "worker1", "worker1" will run the lambda function accordingly
2025-03-04T20:59:00.0439045Z     >>>     # and set the value for the previously returned `Future`, which
2025-03-04T20:59:00.0439620Z     >>>     # will then trigger RPC to send the result back to "worker0".
2025-03-04T20:59:00.0440114Z     >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:00.0440542Z     >>>         lambda fut: fut.wait() + z
2025-03-04T20:59:00.0440857Z     >>>     )
2025-03-04T20:59:00.0441096Z     >>>
2025-03-04T20:59:00.0441322Z     >>> # On worker0
2025-03-04T20:59:00.0441596Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:00.0441889Z     >>> ret = rpc.rpc_sync(
2025-03-04T20:59:00.0442178Z     >>>     "worker1",
2025-03-04T20:59:00.0442455Z     >>>     async_add_chained,
2025-03-04T20:59:00.0442779Z     >>>     args=("worker2", torch.ones(2), 1, 1)
2025-03-04T20:59:00.0443122Z     >>> )
2025-03-04T20:59:00.0443385Z     >>> print(ret)  # prints tensor([3., 3.])
2025-03-04T20:59:00.0443625Z 
2025-03-04T20:59:00.0443876Z     When combined with TorchScript decorators, this decorator must be the
2025-03-04T20:59:00.0444341Z     outmost one.
2025-03-04T20:59:00.0444491Z 
2025-03-04T20:59:00.0444622Z     >>> from torch import Tensor
2025-03-04T20:59:00.0444957Z     >>> from torch.futures import Future
2025-03-04T20:59:00.0445326Z     >>> from torch.distributed import rpc
2025-03-04T20:59:00.0445699Z     >>>
2025-03-04T20:59:00.0445940Z     >>> # omitting setup and shutdown RPC
2025-03-04T20:59:00.0446268Z     >>>
2025-03-04T20:59:00.0446504Z     >>> # On all workers
2025-03-04T20:59:00.0446820Z     >>> @torch.jit.script
2025-03-04T20:59:00.0447164Z     >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2025-03-04T20:59:00.0447535Z     >>>     return x + y
2025-03-04T20:59:00.0447809Z     >>>
2025-03-04T20:59:00.0448065Z     >>> @rpc.functions.async_execution
2025-03-04T20:59:00.0448407Z     >>> @torch.jit.script
2025-03-04T20:59:00.0448788Z     >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2025-03-04T20:59:00.0449258Z     >>>     return rpc.rpc_async(to, script_add, (x, y))
2025-03-04T20:59:00.0449615Z     >>>
2025-03-04T20:59:00.0449842Z     >>> # On worker0
2025-03-04T20:59:00.0450110Z     >>> ret = rpc.rpc_sync(
2025-03-04T20:59:00.0450396Z     >>>     "worker1",
2025-03-04T20:59:00.0450656Z     >>>     async_add,
2025-03-04T20:59:00.0450941Z     >>>     args=("worker2", torch.ones(2), 1)
2025-03-04T20:59:00.0451275Z     >>> )
2025-03-04T20:59:00.0467911Z     >>> print(ret)  # prints tensor([2., 2.])
2025-03-04T20:59:00.0468232Z 
2025-03-04T20:59:00.0468488Z     When combined with static or class method, this decorator must be the
2025-03-04T20:59:00.0468947Z     inner one.
2025-03-04T20:59:00.0469090Z 
2025-03-04T20:59:00.0469231Z     >>> from torch.distributed import rpc
2025-03-04T20:59:00.0469569Z     >>>
2025-03-04T20:59:00.0469826Z     >>> # omitting setup and shutdown RPC
2025-03-04T20:59:00.0470147Z     >>>
2025-03-04T20:59:00.0470477Z     >>> # On all workers
2025-03-04T20:59:00.0470784Z     >>> class AsyncExecutionClass:
2025-03-04T20:59:00.0471100Z     >>>
2025-03-04T20:59:00.0471331Z     >>>     @staticmethod
2025-03-04T20:59:00.0471630Z     >>>     @rpc.functions.async_execution
2025-03-04T20:59:00.0471988Z     >>>     def static_async_add(to, x, y, z):
2025-03-04T20:59:00.0472409Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:00.0472841Z     >>>             lambda fut: fut.wait() + z
2025-03-04T20:59:00.0473173Z     >>>         )
2025-03-04T20:59:00.0473421Z     >>>
2025-03-04T20:59:00.0473864Z     >>>     @classmethod
2025-03-04T20:59:00.0474181Z     >>>     @rpc.functions.async_execution
2025-03-04T20:59:00.0474557Z     >>>     def class_async_add(cls, to, x, y, z):
2025-03-04T20:59:00.0474943Z     >>>         ret_fut = torch.futures.Future()
2025-03-04T20:59:00.0475352Z     >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:00.0475782Z     >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2025-03-04T20:59:00.0476155Z     >>>         )
2025-03-04T20:59:00.0476414Z     >>>         return ret_fut
2025-03-04T20:59:00.0476701Z     >>>
2025-03-04T20:59:00.0476961Z     >>>     @rpc.functions.async_execution
2025-03-04T20:59:00.0477438Z     >>>     def bound_async_add(self, to, x, y, z):
2025-03-04T20:59:00.0477870Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:00.0478294Z     >>>             lambda fut: fut.wait() + z
2025-03-04T20:59:00.0478628Z     >>>         )
2025-03-04T20:59:00.0478874Z     >>>
2025-03-04T20:59:00.0479103Z     >>> # On worker0
2025-03-04T20:59:00.0479374Z     >>> ret = rpc.rpc_sync(
2025-03-04T20:59:00.0479664Z     >>>     "worker1",
2025-03-04T20:59:00.0479973Z     >>>     AsyncExecutionClass.static_async_add,
2025-03-04T20:59:00.0480356Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-04T20:59:00.0480692Z     >>> )
2025-03-04T20:59:00.0480940Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:00.0481278Z     >>>
2025-03-04T20:59:00.0481514Z     >>> ret = rpc.rpc_sync(
2025-03-04T20:59:00.0481803Z     >>>     "worker1",
2025-03-04T20:59:00.0482106Z     >>>     AsyncExecutionClass.class_async_add,
2025-03-04T20:59:00.0482488Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-04T20:59:00.0482825Z     >>> )
2025-03-04T20:59:00.0483084Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:00.0483387Z 
2025-03-04T20:59:00.0483560Z     This decorator also works with RRef helpers, i.e., .
2025-03-04T20:59:00.0483997Z     :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2025-03-04T20:59:00.0484473Z     :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2025-03-04T20:59:00.0484907Z     :meth:`torch.distributed.rpc.RRef.remote`.
2025-03-04T20:59:00.0485163Z 
2025-03-04T20:59:00.0485306Z     >>> from torch.distributed import rpc
2025-03-04T20:59:00.0485646Z     >>>
2025-03-04T20:59:00.0485924Z     >>> # reuse the AsyncExecutionClass class above
2025-03-04T20:59:00.0486356Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-04T20:59:00.0486858Z     >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2025-03-04T20:59:00.0487332Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:00.0487671Z     >>>
2025-03-04T20:59:00.0487953Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-04T20:59:00.0488476Z     >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2025-03-04T20:59:00.0488974Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:00.0489309Z     >>>
2025-03-04T20:59:00.0489603Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-04T20:59:00.0490124Z     >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2025-03-04T20:59:00.0490622Z     >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:00.0490870Z 
2025-03-04T20:59:00.0491132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.0491525Z 
2025-03-04T20:59:00.0492331Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108.
2025-03-04T20:59:00.0493445Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.0493846Z 
2025-03-04T20:59:00.0494061Z Set device mapping between each RPC caller and callee pair. This
2025-03-04T20:59:00.0494591Z function can be called multiple times to incrementally add
2025-03-04T20:59:00.0495033Z device placement configurations.
2025-03-04T20:59:00.0495256Z 
2025-03-04T20:59:00.0495346Z Args:
2025-03-04T20:59:00.0495593Z     to (str): Callee name.
2025-03-04T20:59:00.0496149Z     device_map (Dict of int, str, or torch.device): Device placement
2025-03-04T20:59:00.0496737Z         mappings from this worker to the callee. This map must be
2025-03-04T20:59:00.0497353Z         invertible.
2025-03-04T20:59:00.0497632Z 
2025-03-04T20:59:00.0497925Z Example:
2025-03-04T20:59:00.0498274Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:00.0498612Z     >>> # both workers
2025-03-04T20:59:00.0498883Z     >>> def add(x, y):
2025-03-04T20:59:00.0499198Z     >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2025-03-04T20:59:00.0499643Z     >>>     return x + y, (x + y).to(2)
2025-03-04T20:59:00.0499963Z     >>>
2025-03-04T20:59:00.0500187Z     >>> # on worker 0
2025-03-04T20:59:00.0500500Z     >>> options = TensorPipeRpcBackendOptions(
2025-03-04T20:59:00.0500871Z     >>>     num_worker_threads=8,
2025-03-04T20:59:00.0501205Z     >>>     device_maps={"worker1": {0: 1}}
2025-03-04T20:59:00.0501577Z     >>> # maps worker0's cuda:0 to worker1's cuda:1
2025-03-04T20:59:00.0501922Z     >>> )
2025-03-04T20:59:00.0502187Z     >>> options.set_device_map("worker1", {1: 2})
2025-03-04T20:59:00.0502575Z     >>> # maps worker0's cuda:1 to worker1's cuda:2
2025-03-04T20:59:00.0502920Z     >>>
2025-03-04T20:59:00.0503149Z     >>> rpc.init_rpc(
2025-03-04T20:59:00.0503422Z     >>>     "worker0",
2025-03-04T20:59:00.0503684Z     >>>     rank=0,
2025-03-04T20:59:00.0503940Z     >>>     world_size=2,
2025-03-04T20:59:00.0504262Z     >>>     backend=rpc.BackendType.TENSORPIPE,
2025-03-04T20:59:00.0504643Z     >>>     rpc_backend_options=options
2025-03-04T20:59:00.0504964Z     >>> )
2025-03-04T20:59:00.0505175Z     >>>
2025-03-04T20:59:00.0505447Z     >>> x = torch.ones(2)
2025-03-04T20:59:00.0505795Z     >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2025-03-04T20:59:00.0506276Z     >>> # The first argument will be moved to cuda:1 on worker1. When
2025-03-04T20:59:00.0506818Z     >>> # sending the return value back, it will follow the invert of
2025-03-04T20:59:00.0507316Z     >>> # the device map, and hence will be moved back to cuda:0 and
2025-03-04T20:59:00.0507725Z     >>> # cuda:1 on worker0
2025-03-04T20:59:00.0508069Z     >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2025-03-04T20:59:00.0508499Z     >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2025-03-04T20:59:00.0508782Z 
2025-03-04T20:59:00.0509050Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.0509441Z 
2025-03-04T20:59:00.0510224Z 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.
2025-03-04T20:59:00.0511363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.0511764Z 
2025-03-04T20:59:00.0511974Z It has the same API as ``torch.autograd.profiler.profile`` class,
2025-03-04T20:59:00.0512591Z except that it enables profiling on all threads running RPC server request callbacks.
2025-03-04T20:59:00.0513009Z 
2025-03-04T20:59:00.0513300Z Context manager that manages autograd profiler state and holds a summary of results.
2025-03-04T20:59:00.0513955Z Under the hood it just records events of functions being executed in C++ and
2025-03-04T20:59:00.0514597Z exposes those events to Python. You can wrap any code into it and it will
2025-03-04T20:59:00.0515094Z only report runtime of PyTorch functions.
2025-03-04T20:59:00.0515612Z Note: profiler is thread local and is automatically propagated into the async tasks
2025-03-04T20:59:00.0516023Z 
2025-03-04T20:59:00.0516113Z Args:
2025-03-04T20:59:00.0516526Z     enabled (bool, optional): Setting this to False makes this context manager a no-op.
2025-03-04T20:59:00.0517042Z         Default: ``True``.
2025-03-04T20:59:00.0517235Z 
2025-03-04T20:59:00.0517530Z     use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
2025-03-04T20:59:00.0518158Z         Adds approximately 4us of overhead to each tensor operation.
2025-03-04T20:59:00.0518591Z         Default: ``False``
2025-03-04T20:59:00.0518783Z 
2025-03-04T20:59:00.0519020Z     record_shapes (bool, optional): If shapes recording is set, information
2025-03-04T20:59:00.0519622Z         about input dimensions will be collected. This allows one to see which
2025-03-04T20:59:00.0520218Z         dimensions have been used under the hood and further group by them
2025-03-04T20:59:00.0520789Z         using prof.key_averages(group_by_input_shape=True). Please note that
2025-03-04T20:59:00.0521532Z         shape recording might skew your profiling data. It is recommended to
2025-03-04T20:59:00.0522134Z         use separate runs with and without shape recording to validate the timing.
2025-03-04T20:59:00.0522742Z         Most likely the skew will be negligible for bottom most events (in a case
2025-03-04T20:59:00.0523328Z         of nested function calls). But for higher level functions the total
2025-03-04T20:59:00.0523891Z         self cpu time might be artificially increased because of the shape
2025-03-04T20:59:00.0524327Z         collection.
2025-03-04T20:59:00.0524483Z 
2025-03-04T20:59:00.0524774Z     profile_memory (bool, optional): Whether to report memory usage, default: ``False``
2025-03-04T20:59:00.0525171Z 
2025-03-04T20:59:00.0525277Z .. warning:
2025-03-04T20:59:00.0525630Z     Enabling memory profiling incurs additional profiler overhead
2025-03-04T20:59:00.0525954Z 
2025-03-04T20:59:00.0526061Z .. warning:
2025-03-04T20:59:00.0526460Z     Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
2025-03-04T20:59:00.0527053Z     one cannot use the profiler with ``use_cuda = True`` to benchmark
2025-03-04T20:59:00.0527661Z     DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
2025-03-04T20:59:00.0528208Z     please use ``use_cuda = False`` or ``num_workers = 0``.
2025-03-04T20:59:00.0528505Z 
2025-03-04T20:59:00.0528628Z Example:
2025-03-04T20:59:00.0528875Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:00.0529162Z     >>> # On worker 0:
2025-03-04T20:59:00.0529435Z     >>> import torch
2025-03-04T20:59:00.0529737Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:00.0530134Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:59:00.0530537Z     >>> x, y = torch.tensor(1), torch.tensor(2)
2025-03-04T20:59:00.0530909Z     >>> outer_profile_rref = rpc.remote(
2025-03-04T20:59:00.0531316Z     ...     dst_worker_name, rpc._server_process_global_profile
2025-03-04T20:59:00.0531689Z     ... )
2025-03-04T20:59:00.0531963Z     >>> outer_profile_rref.rpc_sync().__enter__()
2025-03-04T20:59:00.0532377Z     >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
2025-03-04T20:59:00.0532781Z     >>> inner_profile_rref = rpc.remote(
2025-03-04T20:59:00.0533184Z     ...     dst_worker_name, rpc._server_process_global_profile
2025-03-04T20:59:00.0533566Z     ... )
2025-03-04T20:59:00.0533837Z     >>> inner_profile_rref.rpc_sync().__enter__()
2025-03-04T20:59:00.0534251Z     >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
2025-03-04T20:59:00.0534702Z     >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-04T20:59:00.0535161Z     >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-04T20:59:00.0535618Z     >>> print(inner_profile_rref.rpc_sync().key_averages())
2025-03-04T20:59:00.0536171Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:00.0536845Z     Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-04T20:59:00.0537512Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:00.0538133Z     sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
2025-03-04T20:59:00.0538645Z     empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
2025-03-04T20:59:00.0539194Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:00.0539658Z     Self CPU time total: 89.667us
2025-03-04T20:59:00.0540031Z     >>> print(outer_profile_rref.rpc_sync().key_averages())
2025-03-04T20:59:00.0540529Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:00.0541189Z     Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-04T20:59:00.0541887Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:00.0542423Z     sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
2025-03-04T20:59:00.0542922Z     empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
2025-03-04T20:59:00.0543424Z     add        51.68%           110.550us        58.09%           124.259us        124.259us        1
2025-03-04T20:59:00.0543968Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:00.0544431Z     Self CPU time total: 213.926us
2025-03-04T20:59:00.0544745Z     >>> rpc.shutdown()
2025-03-04T20:59:00.0544911Z 
2025-03-04T20:59:00.0545011Z     >>> # On worker 1:
2025-03-04T20:59:00.0545303Z     >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:00.0545691Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:59:00.0546106Z     >>> # wait for worker 0 to finish work, and then shutdown.
2025-03-04T20:59:00.0546538Z     >>> rpc.shutdown()
2025-03-04T20:59:00.0546708Z 
2025-03-04T20:59:00.0546972Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.0547357Z 
2025-03-04T20:59:00.1654712Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=33.
2025-03-04T20:59:00.1655775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.1656165Z 
2025-03-04T20:59:00.1656452Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2025-03-04T20:59:00.1657146Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2025-03-04T20:59:00.1657904Z the local components of :class:`DTensor`, call the function, and wrap the outputs to
2025-03-04T20:59:00.1658480Z :class:`DTensor` according to the ``out_placements``.
2025-03-04T20:59:00.1658758Z 
2025-03-04T20:59:00.1658870Z Args:
2025-03-04T20:59:00.1659210Z     func (Callable): the function to be applied on each local shard of
2025-03-04T20:59:00.1659659Z         :class:`DTensor` s.
2025-03-04T20:59:00.1660090Z     out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2025-03-04T20:59:00.1660711Z         the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2025-03-04T20:59:00.1661348Z         If the flattened ``output`` is a single value, the ``out_placements`` should be
2025-03-04T20:59:00.1661973Z         of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2025-03-04T20:59:00.1662690Z         values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2025-03-04T20:59:00.1663210Z         mapping to the flattened ``output``.
2025-03-04T20:59:00.1663671Z         Besides, for :class:`Tensor` output, we use `PlacementType` as its
2025-03-04T20:59:00.1664297Z         placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2025-03-04T20:59:00.1664819Z         should be `None`.
2025-03-04T20:59:00.1665251Z         Note that the only exception is when no :class:`DTensor` argument is passed
2025-03-04T20:59:00.1665856Z         in. In this case, even if `out_placements` is not `None`, the result function
2025-03-04T20:59:00.1666471Z         should ignore the desired placements because the function is not running with
2025-03-04T20:59:00.1666966Z         :class:`DTensor` s.
2025-03-04T20:59:00.1667330Z     in_placements (Tuple[`PlacementType`, ...], optional):
2025-03-04T20:59:00.1667912Z         the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2025-03-04T20:59:00.1668565Z         If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2025-03-04T20:59:00.1669170Z         placements of each :class:`DTensor` argument is the same as the required
2025-03-04T20:59:00.1669774Z         placements or not. If the placements are not the same and
2025-03-04T20:59:00.1670342Z         ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2025-03-04T20:59:00.1670975Z         ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2025-03-04T20:59:00.1671769Z         the required sharding placements before passing its local tensor to ``func``.
2025-03-04T20:59:00.1672406Z         The only exception is when required placements are not ``None`` and the
2025-03-04T20:59:00.1673023Z         argument is a :class:`torch.Tensor`. In this case, the placements examination
2025-03-04T20:59:00.1673829Z         will be skipped and the argument will be directly passed to ``func``.
2025-03-04T20:59:00.1674426Z         If ``in_placements`` is ``None``, no placements examination will be performed.
2025-03-04T20:59:00.1674891Z         Default: None
2025-03-04T20:59:00.1675208Z     device_mesh (:class:`DeviceMesh`, optional):
2025-03-04T20:59:00.1675682Z         the device mesh that all the :class:`DTensor` s are placed on. If not
2025-03-04T20:59:00.1676340Z         specified, this will be inferred from the input :class:`DTensor` s' device
2025-03-04T20:59:00.1676963Z         mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
2025-03-04T20:59:00.1677502Z         device mesh. Default: None.
2025-03-04T20:59:00.1677857Z     redistribute_inputs (bool, optional):
2025-03-04T20:59:00.1678362Z         the bool value indicating whether to reshard the input :class:`DTensor` s when
2025-03-04T20:59:00.1679010Z         their placements are different from the required input placements. If this
2025-03-04T20:59:00.1679636Z         value is ``False`` and some :class:`DTensor` input has a different placement,
2025-03-04T20:59:00.1680153Z         an exception will be raised. Default: False.
2025-03-04T20:59:00.1680426Z 
2025-03-04T20:59:00.1680523Z Returns:
2025-03-04T20:59:00.1680928Z     A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2025-03-04T20:59:00.1681571Z     and returns a :class:`DTensor` constructed from the return value of ``func``.
2025-03-04T20:59:00.1681954Z 
2025-03-04T20:59:00.1682051Z Raises:
2025-03-04T20:59:00.1682453Z     AssertionError: If the input :class:`DTensor` is not placed on the same device
2025-03-04T20:59:00.1683098Z         mesh, or if they are placed on a different device mesh than the ``device_mesh``
2025-03-04T20:59:00.1683587Z         argument passed in.
2025-03-04T20:59:00.1683776Z 
2025-03-04T20:59:00.1684045Z     AssertionError: For any non-DTensor output, we require its corresponding
2025-03-04T20:59:00.1684692Z         output placement in ``out_placements`` be None. An AssertionError will be raised
2025-03-04T20:59:00.1685252Z         if this is not the case.
2025-03-04T20:59:00.1685457Z 
2025-03-04T20:59:00.1685744Z     ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2025-03-04T20:59:00.1686313Z         a redistribution according to ``in_placements``.
2025-03-04T20:59:00.1686585Z 
2025-03-04T20:59:00.1686731Z Example:
2025-03-04T20:59:00.1686994Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:00.1687381Z     >>> def mm_allreduce_forward(device_mesh, W, X):
2025-03-04T20:59:00.1687781Z     >>>     partial_sum_tensor = torch.mm(W, X)
2025-03-04T20:59:00.1688280Z     >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2025-03-04T20:59:00.1688774Z     >>>     return reduced_tensor
2025-03-04T20:59:00.1689074Z     >>>
2025-03-04T20:59:00.1689341Z     >>> W = torch.randn(12, 8, requires_grad=False)
2025-03-04T20:59:00.1689726Z     >>> X = torch.randn(8, 16, requires_grad=False)
2025-03-04T20:59:00.1690086Z     >>> Y = torch.mm(W, X)
2025-03-04T20:59:00.1690469Z     >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2025-03-04T20:59:00.1690987Z     >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2025-03-04T20:59:00.1691429Z     >>>
2025-03-04T20:59:00.1691842Z     >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
2025-03-04T20:59:00.1692390Z     >>> local_mm_allreduce_forward = local_map(
2025-03-04T20:59:00.1692757Z     >>>     mm_allreduce_forward,
2025-03-04T20:59:00.1693095Z     >>>     out_placements=[Replicate()],
2025-03-04T20:59:00.1693469Z     >>>     in_placements=[col_wise, row_wise],
2025-03-04T20:59:00.1693828Z     >>>     device_mesh=device_mesh,
2025-03-04T20:59:00.1694140Z     >>> )
2025-03-04T20:59:00.1694365Z     >>>
2025-03-04T20:59:00.1694606Z     >>> W_dt = distribute_tensor(
2025-03-04T20:59:00.1694930Z     ...     W, device_mesh, (col_wise)
2025-03-04T20:59:00.1695271Z     ... )  # col-wisely sharded W tensor
2025-03-04T20:59:00.1695614Z     >>> X_dt = distribute_tensor(
2025-03-04T20:59:00.1695935Z     ...     X, device_mesh, (row_wise)
2025-03-04T20:59:00.1696274Z     ... )  # row-wisely sharded X tensor
2025-03-04T20:59:00.1696628Z     >>> Y_dt = local_mm_allreduce_forward(
2025-03-04T20:59:00.1697011Z     ...     device_mesh, W_dt, X_dt
2025-03-04T20:59:00.1697381Z     ... )  # apply local_mm_allreduce_forward to DTensors
2025-03-04T20:59:00.1697691Z 
2025-03-04T20:59:00.1698020Z .. note:: This API is currently experimental and subject to change
2025-03-04T20:59:00.1698354Z 
2025-03-04T20:59:00.1698649Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.1699044Z 
2025-03-04T20:59:00.1699774Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=26.
2025-03-04T20:59:00.1700891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.1701295Z 
2025-03-04T20:59:00.1701598Z :meth:`register_sharding` is an experimental API that allows users to register sharding
2025-03-04T20:59:00.1702247Z strategies for an operator when the tensor inputs and outputs are DTensor.
2025-03-04T20:59:00.1702880Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2025-03-04T20:59:00.1703522Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2025-03-04T20:59:00.1704183Z when users would like to overwrite default sharding strategies of existing operators.
2025-03-04T20:59:00.1704597Z 
2025-03-04T20:59:00.1704689Z Args:
2025-03-04T20:59:00.1704956Z     op (Union[OpOverload, List[OpOverload]]):
2025-03-04T20:59:00.1705417Z         An op or a list of ops to register the customized sharding function.
2025-03-04T20:59:00.1705746Z 
2025-03-04T20:59:00.1705841Z Returns:
2025-03-04T20:59:00.1706253Z     A function decorator which can be used to wrap a function that defines the sharding
2025-03-04T20:59:00.1706968Z     strategy for the operator specified in ``op``. The defined sharding strategy will be
2025-03-04T20:59:00.1707667Z     registered to DTensor and will override the default sharding strategy if DTensor has
2025-03-04T20:59:00.1708391Z     already implemented the operator. The customized sharding function takes the same inputs
2025-03-04T20:59:00.1709081Z     as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2025-03-04T20:59:00.1709735Z     replaced by a tensor-like object that DTensor uses internally). The function should
2025-03-04T20:59:00.1710427Z     return a sequence of 2-tuples, each specifying acceptable output placements and its
2025-03-04T20:59:00.1710959Z     corresponding intput placements.
2025-03-04T20:59:00.1711193Z 
2025-03-04T20:59:00.1711288Z Example:
2025-03-04T20:59:00.1711541Z     >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:00.1711917Z     >>> @register_sharding(aten._softmax.default)
2025-03-04T20:59:00.1712378Z     >>> def custom_softmax_sharding(x, dim, half_to_float):
2025-03-04T20:59:00.1712817Z     >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2025-03-04T20:59:00.1713208Z     >>>     acceptable_shardings = []
2025-03-04T20:59:00.1713533Z     >>>
2025-03-04T20:59:00.1713894Z     >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2025-03-04T20:59:00.1714363Z     >>>     acceptable_shardings.append(all_replicate)
2025-03-04T20:59:00.1714731Z     >>>
2025-03-04T20:59:00.1714980Z     >>>     for sharding_dim in range(x.ndim):
2025-03-04T20:59:00.1715352Z     >>>         if sharding_dim != softmax_dim:
2025-03-04T20:59:00.1715704Z     >>>             all_sharded = (
2025-03-04T20:59:00.1716036Z     >>>                 [Shard(sharding_dim)],
2025-03-04T20:59:00.1716407Z     >>>                 [Shard(sharding_dim), None, None],
2025-03-04T20:59:00.1716756Z     >>>             )
2025-03-04T20:59:00.1717071Z     >>>             acceptable_shardings.append(all_sharded)
2025-03-04T20:59:00.1717433Z     >>>
2025-03-04T20:59:00.1717685Z     >>>     return acceptable_shardings
2025-03-04T20:59:00.1717910Z 
2025-03-04T20:59:00.1718150Z .. note:: This API is currently experimental and subject to change
2025-03-04T20:59:00.1718464Z 
2025-03-04T20:59:00.1718738Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.1719114Z 
2025-03-04T20:59:00.1935505Z 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=403.
2025-03-04T20:59:00.1936695Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.1937106Z 
2025-03-04T20:59:00.1937499Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2025-03-04T20:59:00.1938431Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2025-03-04T20:59:00.1938892Z 
2025-03-04T20:59:00.1938998Z Keyword Args:
2025-03-04T20:59:00.1939442Z     input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-04T20:59:00.1940195Z         The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-03-04T20:59:00.1941123Z         DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-03-04T20:59:00.1941754Z         as a placeholder. default: None.
2025-03-04T20:59:00.1942240Z     desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-04T20:59:00.1942996Z         The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-04T20:59:00.1943946Z         have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-03-04T20:59:00.1944620Z     input_kwarg_layouts (Dict[str, Placement]):
2025-03-04T20:59:00.1945446Z         The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-03-04T20:59:00.1946063Z         default: None
2025-03-04T20:59:00.1946406Z     desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-03-04T20:59:00.1947071Z         The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-04T20:59:00.1947734Z         have the desired DTensor layouts. default: None.
2025-03-04T20:59:00.1948130Z     use_local_output (bool, optional):
2025-03-04T20:59:00.1948736Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-03-04T20:59:00.1949321Z Returns:
2025-03-04T20:59:00.1949781Z     A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2025-03-04T20:59:00.1950221Z 
2025-03-04T20:59:00.1950342Z Example::
2025-03-04T20:59:00.1950592Z     >>> # xdoctest: +SKIP(failing)
2025-03-04T20:59:00.1951133Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2025-03-04T20:59:00.1951783Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-04T20:59:00.1952199Z     >>> ...
2025-03-04T20:59:00.1952652Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-04T20:59:00.1953304Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-04T20:59:00.1953656Z     >>>
2025-03-04T20:59:00.1954125Z     >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2025-03-04T20:59:00.1954753Z     >>> # and then redistributed to Replicated DTensor.
2025-03-04T20:59:00.1955137Z     >>> parallelize_module(
2025-03-04T20:59:00.1955477Z     >>>     block, # this can be a submodule or module
2025-03-04T20:59:00.1955832Z     >>>     tp_mesh,
2025-03-04T20:59:00.1956107Z     >>>     parallelize_plan={
2025-03-04T20:59:00.1956433Z     >>>         "attn": PrepareModuleInput(
2025-03-04T20:59:00.1956819Z     >>>             input_layouts=(Shard(0), None, None, ...),
2025-03-04T20:59:00.1957259Z     >>>             desired_input_layouts=(Replicate(), None, None, ...)
2025-03-04T20:59:00.1957652Z     >>>         ),
2025-03-04T20:59:00.1957902Z     >>>     }
2025-03-04T20:59:00.1958139Z     >>> )
2025-03-04T20:59:00.1958264Z 
2025-03-04T20:59:00.1958540Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.1958963Z 
2025-03-04T20:59:00.1959680Z 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=562.
2025-03-04T20:59:00.1960743Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.1961130Z 
2025-03-04T20:59:00.1961545Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2025-03-04T20:59:00.1962415Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2025-03-04T20:59:00.1962872Z 
2025-03-04T20:59:00.1962983Z Keyword Args:
2025-03-04T20:59:00.1963307Z     output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-04T20:59:00.1963946Z         The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-03-04T20:59:00.1964811Z         DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-03-04T20:59:00.1965474Z         ``None`` need to be specified as a placeholder.
2025-03-04T20:59:00.1965949Z     desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-04T20:59:00.1966675Z         The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-03-04T20:59:00.1967324Z         have the desired DTensor layouts.
2025-03-04T20:59:00.1967686Z     use_local_output (bool, optional):
2025-03-04T20:59:00.1968318Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-03-04T20:59:00.1968904Z Returns:
2025-03-04T20:59:00.1969330Z     A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2025-03-04T20:59:00.1969765Z 
2025-03-04T20:59:00.1969864Z Example::
2025-03-04T20:59:00.1970111Z     >>> # xdoctest: +SKIP(failing)
2025-03-04T20:59:00.1970653Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2025-03-04T20:59:00.1971303Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-04T20:59:00.1971713Z     >>> ...
2025-03-04T20:59:00.1972164Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-04T20:59:00.1972737Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-04T20:59:00.1973078Z     >>>
2025-03-04T20:59:00.1973822Z     >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2025-03-04T20:59:00.1974511Z     >>> # and then redistributed to Sharded DTensor.
2025-03-04T20:59:00.1974884Z     >>> parallelize_module(
2025-03-04T20:59:00.1975222Z     >>>     block, # this can be a submodule or module
2025-03-04T20:59:00.1975655Z     >>>     tp_mesh,
2025-03-04T20:59:00.1975969Z     >>>     parallelize_plan = PrepareModuleOutput(
2025-03-04T20:59:00.1976365Z     >>>         output_layouts=Replicate(),
2025-03-04T20:59:00.1976736Z     >>>         desired_output_layouts=Shard(0)
2025-03-04T20:59:00.1977076Z     >>>     )
2025-03-04T20:59:00.1977314Z     >>> )
2025-03-04T20:59:00.1977439Z 
2025-03-04T20:59:00.1977715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.1978159Z 
2025-03-04T20:59:00.2516603Z 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=55.
2025-03-04T20:59:00.2517755Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.2518145Z 
2025-03-04T20:59:00.2518472Z Creates a multivariate normal distribution with covariance matrix having a low-rank form
2025-03-04T20:59:00.2519134Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
2025-03-04T20:59:00.2519438Z 
2025-03-04T20:59:00.2519637Z     covariance_matrix = cov_factor @ cov_factor.T + cov_diag
2025-03-04T20:59:00.2520106Z 
2025-03-04T20:59:00.2520220Z Example:
2025-03-04T20:59:00.2520515Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-03-04T20:59:00.2521000Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:00.2521398Z     >>> m = LowRankMultivariateNormal(
2025-03-04T20:59:00.2521833Z     ...     torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2)
2025-03-04T20:59:00.2522228Z     ... )
2025-03-04T20:59:00.2522666Z     >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
2025-03-04T20:59:00.2523218Z     tensor([-0.2102, -0.5429])
2025-03-04T20:59:00.2523427Z 
2025-03-04T20:59:00.2523518Z Args:
2025-03-04T20:59:00.2523902Z     loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
2025-03-04T20:59:00.2524547Z     cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
2025-03-04T20:59:00.2525076Z         `batch_shape + event_shape + (rank,)`
2025-03-04T20:59:00.2525594Z     cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
2025-03-04T20:59:00.2526102Z         `batch_shape + event_shape`
2025-03-04T20:59:00.2526329Z 
2025-03-04T20:59:00.2526425Z Note:
2025-03-04T20:59:00.2526834Z     The computation for determinant and inverse of covariance matrix is avoided when
2025-03-04T20:59:00.2527494Z     `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
2025-03-04T20:59:00.2528095Z     <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
2025-03-04T20:59:00.2528778Z     `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
2025-03-04T20:59:00.2529469Z     Thanks to these formulas, we just need to compute the determinant and inverse of
2025-03-04T20:59:00.2529988Z     the small size "capacitance" matrix::
2025-03-04T20:59:00.2530225Z 
2025-03-04T20:59:00.2530429Z         capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
2025-03-04T20:59:00.2530732Z 
2025-03-04T20:59:00.2531008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.2531383Z 
2025-03-04T20:59:00.2534791Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13.
2025-03-04T20:59:00.2535084Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.2535090Z 
2025-03-04T20:59:00.2535322Z The `MixtureSameFamily` distribution implements a (batch of) mixture
2025-03-04T20:59:00.2535595Z distribution where all component are from different parameterizations of
2025-03-04T20:59:00.2535817Z the same distribution type. It is parameterized by a `Categorical`
2025-03-04T20:59:00.2536040Z "selecting distribution" (over `k` component) and a component
2025-03-04T20:59:00.2536343Z distribution, i.e., a `Distribution` with a rightmost batch shape
2025-03-04T20:59:00.2536526Z (equal to `[k]`) which indexes each (batch of) component.
2025-03-04T20:59:00.2536535Z 
2025-03-04T20:59:00.2536647Z Examples::
2025-03-04T20:59:00.2536651Z 
2025-03-04T20:59:00.2536792Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:00.2537007Z     >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2025-03-04T20:59:00.2537145Z     >>> # weighted normal distributions
2025-03-04T20:59:00.2537275Z     >>> mix = D.Categorical(torch.ones(5,))
2025-03-04T20:59:00.2537447Z     >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2025-03-04T20:59:00.2537577Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-03-04T20:59:00.2537582Z 
2025-03-04T20:59:00.2537871Z     >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2025-03-04T20:59:00.2538014Z     >>> # weighted bivariate normal distributions
2025-03-04T20:59:00.2538156Z     >>> mix = D.Categorical(torch.ones(5,))
2025-03-04T20:59:00.2538281Z     >>> comp = D.Independent(D.Normal(
2025-03-04T20:59:00.2538433Z     ...          torch.randn(5,2), torch.rand(5,2)), 1)
2025-03-04T20:59:00.2538608Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-03-04T20:59:00.2538627Z 
2025-03-04T20:59:00.2538830Z     >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2025-03-04T20:59:00.2539078Z     >>> # consisting of 5 random weighted bivariate normal distributions
2025-03-04T20:59:00.2539218Z     >>> mix = D.Categorical(torch.rand(3,5))
2025-03-04T20:59:00.2539337Z     >>> comp = D.Independent(D.Normal(
2025-03-04T20:59:00.2539492Z     ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2025-03-04T20:59:00.2539621Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-03-04T20:59:00.2539625Z 
2025-03-04T20:59:00.2539728Z Args:
2025-03-04T20:59:00.2539944Z     mixture_distribution: `torch.distributions.Categorical`-like
2025-03-04T20:59:00.2540156Z         instance. Manages the probability of selecting component.
2025-03-04T20:59:00.2540337Z         The number of categories must match the rightmost batch
2025-03-04T20:59:00.2540550Z         dimension of the `component_distribution`. Must have either
2025-03-04T20:59:00.2540701Z         scalar `batch_shape` or `batch_shape` matching
2025-03-04T20:59:00.2540858Z         `component_distribution.batch_shape[:-1]`
2025-03-04T20:59:00.2541089Z     component_distribution: `torch.distributions.Distribution`-like
2025-03-04T20:59:00.2541289Z         instance. Right-most batch dimension indexes component.
2025-03-04T20:59:00.2541293Z 
2025-03-04T20:59:00.2541554Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.2541559Z 
2025-03-04T20:59:00.2661973Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=111.
2025-03-04T20:59:00.2662272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.2662297Z 
2025-03-04T20:59:00.2662492Z Creates a RelaxedBernoulli distribution, parametrized by
2025-03-04T20:59:00.2662708Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2025-03-04T20:59:00.2662936Z (but not both). This is a relaxed version of the `Bernoulli` distribution,
2025-03-04T20:59:00.2663142Z so the values are in (0, 1), and has reparametrizable samples.
2025-03-04T20:59:00.2663149Z 
2025-03-04T20:59:00.2663259Z Example::
2025-03-04T20:59:00.2663263Z 
2025-03-04T20:59:00.2663424Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:00.2663564Z     >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2025-03-04T20:59:00.2663712Z     ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2025-03-04T20:59:00.2663809Z     >>> m.sample()
2025-03-04T20:59:00.2663946Z     tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2025-03-04T20:59:00.2663951Z 
2025-03-04T20:59:00.2664041Z Args:
2025-03-04T20:59:00.2664199Z     temperature (Tensor): relaxation temperature
2025-03-04T20:59:00.2664425Z     probs (Number, Tensor): the probability of sampling `1`
2025-03-04T20:59:00.2664607Z     logits (Number, Tensor): the log-odds of sampling `1`
2025-03-04T20:59:00.2664613Z 
2025-03-04T20:59:00.2664875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.2664879Z 
2025-03-04T20:59:00.2683090Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=101.
2025-03-04T20:59:00.2683385Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.2683392Z 
2025-03-04T20:59:00.2683638Z Creates a RelaxedOneHotCategorical distribution parametrized by
2025-03-04T20:59:00.2683847Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2025-03-04T20:59:00.2684108Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2025-03-04T20:59:00.2684282Z its samples are on simplex, and are reparametrizable.
2025-03-04T20:59:00.2684291Z 
2025-03-04T20:59:00.2684456Z Example::
2025-03-04T20:59:00.2684461Z 
2025-03-04T20:59:00.2684757Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:00.2684937Z     >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2025-03-04T20:59:00.2685074Z     ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2025-03-04T20:59:00.2685245Z     >>> m.sample()
2025-03-04T20:59:00.2685371Z     tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2025-03-04T20:59:00.2685376Z 
2025-03-04T20:59:00.2685479Z Args:
2025-03-04T20:59:00.2685629Z     temperature (Tensor): relaxation temperature
2025-03-04T20:59:00.2685769Z     probs (Tensor): event probabilities
2025-03-04T20:59:00.2685966Z     logits (Tensor): unnormalized log probability for each event
2025-03-04T20:59:00.2685972Z 
2025-03-04T20:59:00.2686247Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.2686252Z 
2025-03-04T20:59:00.6216040Z 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.
2025-03-04T20:59:00.6217476Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.6218451Z Return a new dict with new, potentially nested, key value pair
2025-03-04T20:59:00.6218862Z 
2025-03-04T20:59:00.6219002Z     >>> purchase = {
2025-03-04T20:59:00.6219318Z     ...     "name": "Alice",
2025-03-04T20:59:00.6219935Z     ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-04T20:59:00.6220621Z     ...     "credit card": "5555-1234-1234-1234",
2025-03-04T20:59:00.6220957Z     ... }
2025-03-04T20:59:00.6221457Z     >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2025-03-04T20:59:00.6222042Z     {'credit card': '5555-1234-1234-1234',
2025-03-04T20:59:00.6222458Z      'name': 'Alice',
2025-03-04T20:59:00.6222902Z      'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2025-03-04T20:59:00.6223680Z     
2025-03-04T20:59:00.6224174Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.6224575Z 
2025-03-04T20:59:00.6225299Z 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.
2025-03-04T20:59:00.6226366Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.6226924Z Update value in a (potentially) nested dictionary
2025-03-04T20:59:00.6227194Z 
2025-03-04T20:59:00.6227308Z     inputs:
2025-03-04T20:59:00.6227580Z     d - dictionary on which to operate
2025-03-04T20:59:00.6228050Z     keys - list or tuple giving the location of the value to be changed in d
2025-03-04T20:59:00.6228544Z     func - function to operate on that value
2025-03-04T20:59:00.6228788Z 
2025-03-04T20:59:00.6229001Z     If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2025-03-04T20:59:00.6229670Z     original dictionary with v replaced by func(v), but does not mutate the
2025-03-04T20:59:00.6230222Z     original dictionary.
2025-03-04T20:59:00.6230471Z 
2025-03-04T20:59:00.6230778Z     If k0 is not a key in d, update_in creates nested dictionaries to the depth
2025-03-04T20:59:00.6231726Z     specified by the keys, with the innermost value set to func(default).
2025-03-04T20:59:00.6232154Z 
2025-03-04T20:59:00.6232269Z     >>> inc = lambda x: x + 1
2025-03-04T20:59:00.6232590Z     >>> update_in({"a": 0}, ["a"], inc)
2025-03-04T20:59:00.6232909Z     {'a': 1}
2025-03-04T20:59:00.6233062Z 
2025-03-04T20:59:00.6233167Z     >>> transaction = {
2025-03-04T20:59:00.6233445Z     ...     "name": "Alice",
2025-03-04T20:59:00.6233836Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-04T20:59:00.6234284Z     ...     "credit card": "5555-1234-1234-1234",
2025-03-04T20:59:00.6234622Z     ... }
2025-03-04T20:59:00.6234979Z     >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2025-03-04T20:59:00.6235448Z     {'credit card': '5555-1234-1234-1234',
2025-03-04T20:59:00.6235886Z      'name': 'Alice',
2025-03-04T20:59:00.6236231Z      'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2025-03-04T20:59:00.6236519Z 
2025-03-04T20:59:00.6236663Z     >>> # updating a value when k0 is not in d
2025-03-04T20:59:00.6237109Z     >>> update_in({}, [1, 2, 3], str, default="bar")
2025-03-04T20:59:00.6237464Z     {1: {2: {3: 'bar'}}}
2025-03-04T20:59:00.6237769Z     >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2025-03-04T20:59:00.6238120Z     {1: 'foo', 2: {3: {4: 1}}}
2025-03-04T20:59:00.6238408Z     
2025-03-04T20:59:00.6238795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.6239193Z 
2025-03-04T20:59:00.6239846Z 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.
2025-03-04T20:59:00.6240902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.6241656Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2025-03-04T20:59:00.6242055Z 
2025-03-04T20:59:00.6242328Z     If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2025-03-04T20:59:00.6242964Z     ``no_default`` is specified, then it raises KeyError or IndexError.
2025-03-04T20:59:00.6243390Z 
2025-03-04T20:59:00.6243717Z     ``get_in`` is a generalization of ``operator.getitem`` for nested data
2025-03-04T20:59:00.6244415Z     structures such as dictionaries and lists.
2025-03-04T20:59:00.6244801Z 
2025-03-04T20:59:00.6244970Z     >>> transaction = {
2025-03-04T20:59:00.6245453Z     ...     "name": "Alice",
2025-03-04T20:59:00.6246117Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-04T20:59:00.6246795Z     ...     "credit card": "5555-1234-1234-1234",
2025-03-04T20:59:00.6247339Z     ... }
2025-03-04T20:59:00.6247814Z     >>> get_in(["purchase", "items", 0], transaction)
2025-03-04T20:59:00.6248178Z     'Apple'
2025-03-04T20:59:00.6248437Z     >>> get_in(["name"], transaction)
2025-03-04T20:59:00.6248756Z     'Alice'
2025-03-04T20:59:00.6249030Z     >>> get_in(["purchase", "total"], transaction)
2025-03-04T20:59:00.6249445Z     >>> get_in(["purchase", "items", "apple"], transaction)
2025-03-04T20:59:00.6249867Z     >>> get_in(["purchase", "items", 10], transaction)
2025-03-04T20:59:00.6250275Z     >>> get_in(["purchase", "total"], transaction, 0)
2025-03-04T20:59:00.6250626Z     0
2025-03-04T20:59:00.6250871Z     >>> get_in(["y"], {}, no_default=True)
2025-03-04T20:59:00.6251227Z     Traceback (most recent call last):
2025-03-04T20:59:00.6251539Z         ...
2025-03-04T20:59:00.6251771Z     KeyError: 'y'
2025-03-04T20:59:00.6251935Z 
2025-03-04T20:59:00.6252028Z     See Also:
2025-03-04T20:59:00.6252276Z         itertoolz.get
2025-03-04T20:59:00.6252553Z         operator.getitem
2025-03-04T20:59:00.6252824Z     
2025-03-04T20:59:00.6253290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.6253683Z 
2025-03-04T20:59:00.6254356Z 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.
2025-03-04T20:59:00.6255412Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:00.6255926Z Group a collection by a key function
2025-03-04T20:59:00.6256156Z 
2025-03-04T20:59:00.6256332Z     >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2025-03-04T20:59:00.6256821Z     >>> groupby(len, names)  # doctest: +SKIP
2025-03-04T20:59:00.6257239Z     {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2025-03-04T20:59:00.6257520Z 
2025-03-04T20:59:00.6257648Z     >>> iseven = lambda x: x % 2 == 0
2025-03-04T20:59:00.6258122Z     >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2025-03-04T20:59:00.6258580Z     {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2025-03-04T20:59:00.6258886Z 
2025-03-04T20:59:00.6259101Z     Non-callable keys imply grouping on a member.
2025-03-04T20:59:00.6259634Z 
2025-03-04T20:59:00.6259817Z     >>> groupby(
2025-03-04T20:59:00.6260211Z     ...     "gender",
2025-03-04T20:59:00.6260620Z     ...     [
2025-03-04T20:59:00.6261105Z     ...         {"name": "Alice", "gender": "F"},
2025-03-04T20:59:00.6261777Z     ...         {"name": "Bob", "gender": "M"},
2025-03-04T20:59:00.6262159Z     ...         {"name": "Charlie", "gender": "M"},
2025-03-04T20:59:00.6262491Z     ...     ],
2025-03-04T20:59:00.6262746Z     ... )  # doctest:+SKIP
2025-03-04T20:59:00.6263053Z     {'F': [{'gender': 'F', 'name': 'Alice'}],
2025-03-04T20:59:00.6263412Z      'M': [{'gender': 'M', 'name': 'Bob'},
2025-03-04T20:59:00.6263770Z            {'gender': 'M', 'name': 'Charlie'}]}
2025-03-04T20:59:00.6264016Z 
2025-03-04T20:59:00.6264167Z     Not to be confused with ``itertools.groupby``
2025-03-04T20:59:00.6264441Z 
2025-03-04T20:59:00.6264537Z     See Also:
2025-03-04T20:59:00.6264777Z         countby
2025-03-04T20:59:00.6265018Z     
2025-03-04T20:59:00.6265410Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:00.6265791Z 
2025-03-04T20:59:01.0115581Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601.
2025-03-04T20:59:01.0116569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.0117160Z Applies Batch Normalization over a N-Dimensional input.
2025-03-04T20:59:01.0117473Z 
2025-03-04T20:59:01.0118056Z     The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2025-03-04T20:59:01.0119016Z     `Batch Normalization: Accelerating Deep Network Training by Reducing
2025-03-04T20:59:01.0119962Z     Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2025-03-04T20:59:01.0120315Z 
2025-03-04T20:59:01.0120438Z     .. math::
2025-03-04T20:59:01.0120592Z 
2025-03-04T20:59:01.0120823Z         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2025-03-04T20:59:01.0121189Z 
2025-03-04T20:59:01.0121427Z     The mean and standard-deviation are calculated per-dimension over all
2025-03-04T20:59:01.0122033Z     mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2025-03-04T20:59:01.0122652Z     are learnable parameter vectors of size `C` (where `C` is the input size).
2025-03-04T20:59:01.0123214Z     By default, the elements of :math:`\gamma` are sampled from
2025-03-04T20:59:01.0123732Z     :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2025-03-04T20:59:01.0124335Z     The standard-deviation is calculated via the biased estimator, equivalent to
2025-03-04T20:59:01.0124851Z     `torch.var(input, unbiased=False)`.
2025-03-04T20:59:01.0125077Z 
2025-03-04T20:59:01.0125334Z     Also by default, during training this layer keeps running estimates of its
2025-03-04T20:59:01.0126048Z     computed mean and variance, which are then used for normalization during
2025-03-04T20:59:01.0126672Z     evaluation. The running estimates are kept with a default :attr:`momentum`
2025-03-04T20:59:01.0127147Z     of 0.1.
2025-03-04T20:59:01.0127278Z 
2025-03-04T20:59:01.0127533Z     If :attr:`track_running_stats` is set to ``False``, this layer then does not
2025-03-04T20:59:01.0128113Z     keep running estimates, and batch statistics are instead used during
2025-03-04T20:59:01.0128595Z     evaluation time as well.
2025-03-04T20:59:01.0128802Z 
2025-03-04T20:59:01.0128901Z     .. note::
2025-03-04T20:59:01.0129285Z         This :attr:`momentum` argument is different from one used in optimizer
2025-03-04T20:59:01.0129880Z         classes and the conventional notion of momentum. Mathematically, the
2025-03-04T20:59:01.0130387Z         update rule for running statistics here is
2025-03-04T20:59:01.0130926Z         :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2025-03-04T20:59:01.0131545Z         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2025-03-04T20:59:01.0132064Z         new observed value.
2025-03-04T20:59:01.0132267Z 
2025-03-04T20:59:01.0132578Z     Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2025-03-04T20:59:01.0133328Z     statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2025-03-04T20:59:01.0133903Z     Normalization or Spatio-temporal Batch Normalization.
2025-03-04T20:59:01.0134214Z 
2025-03-04T20:59:01.0134372Z     Currently :class:`SyncBatchNorm` only supports
2025-03-04T20:59:01.0134946Z     :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2025-03-04T20:59:01.0135595Z     :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2025-03-04T20:59:01.0136159Z     :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2025-03-04T20:59:01.0136610Z     Network with DDP.
2025-03-04T20:59:01.0136776Z 
2025-03-04T20:59:01.0136886Z     Args:
2025-03-04T20:59:01.0137202Z         num_features: :math:`C` from an expected input of size
2025-03-04T20:59:01.0137601Z             :math:`(N, C, +)`
2025-03-04T20:59:01.0138078Z         eps: a value added to the denominator for numerical stability.
2025-03-04T20:59:01.0138510Z             Default: ``1e-5``
2025-03-04T20:59:01.0138913Z         momentum: the value used for the running_mean and running_var
2025-03-04T20:59:01.0139456Z             computation. Can be set to ``None`` for cumulative moving average
2025-03-04T20:59:01.0139930Z             (i.e. simple average). Default: 0.1
2025-03-04T20:59:01.0140411Z         affine: a boolean value that when set to ``True``, this module has
2025-03-04T20:59:01.0140907Z             learnable affine parameters. Default: ``True``
2025-03-04T20:59:01.0141411Z         track_running_stats: a boolean value that when set to ``True``, this
2025-03-04T20:59:01.0141998Z             module tracks the running mean and variance, and when set to ``False``,
2025-03-04T20:59:01.0142596Z             this module does not track such statistics, and initializes statistics
2025-03-04T20:59:01.0143166Z             buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2025-03-04T20:59:01.0143742Z             When these buffers are ``None``, this module always uses batch statistics.
2025-03-04T20:59:01.0144273Z             in both training and eval modes. Default: ``True``
2025-03-04T20:59:01.0144814Z         process_group: synchronization of stats happen within each process group
2025-03-04T20:59:01.0145436Z             individually. Default behavior is synchronization across the whole
2025-03-04T20:59:01.0145893Z             world
2025-03-04T20:59:01.0146056Z 
2025-03-04T20:59:01.0146151Z     Shape:
2025-03-04T20:59:01.0146403Z         - Input: :math:`(N, C, +)`
2025-03-04T20:59:01.0146773Z         - Output: :math:`(N, C, +)` (same shape as input)
2025-03-04T20:59:01.0147084Z 
2025-03-04T20:59:01.0147183Z     .. note::
2025-03-04T20:59:01.0147583Z         Synchronization of batchnorm statistics occurs only while training, i.e.
2025-03-04T20:59:01.0148178Z         synchronization is disabled when ``model.eval()`` is set or if
2025-03-04T20:59:01.0148683Z         ``self.training`` is otherwise ``False``.
2025-03-04T20:59:01.0148941Z 
2025-03-04T20:59:01.0149047Z     Examples::
2025-03-04T20:59:01.0149208Z 
2025-03-04T20:59:01.0149318Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:01.0149646Z         >>> # With Learnable Parameters
2025-03-04T20:59:01.0150005Z         >>> m = nn.SyncBatchNorm(100)
2025-03-04T20:59:01.0150365Z         >>> # creating process group (optional)
2025-03-04T20:59:01.0150770Z         >>> # ranks is a list of int identifying rank ids.
2025-03-04T20:59:01.0151151Z         >>> ranks = list(range(8))
2025-03-04T20:59:01.0151474Z         >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-04T20:59:01.0151857Z         >>> # Note: every rank calls into new_group for every
2025-03-04T20:59:01.0152292Z         >>> # process group created, even if that rank is not
2025-03-04T20:59:01.0152726Z         >>> # part of the group.
2025-03-04T20:59:01.0153185Z         >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-04T20:59:01.0153784Z         >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-04T20:59:01.0154274Z         >>> # Without Learnable Parameters
2025-03-04T20:59:01.0154730Z         >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2025-03-04T20:59:01.0155202Z         >>> input = torch.randn(20, 100, 35, 45, 10)
2025-03-04T20:59:01.0155561Z         >>> output = m(input)
2025-03-04T20:59:01.0155762Z 
2025-03-04T20:59:01.0155888Z         >>> # network is nn.BatchNorm layer
2025-03-04T20:59:01.0156414Z         >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2025-03-04T20:59:01.0156998Z         >>> # only single gpu per process is currently supported
2025-03-04T20:59:01.0157522Z         >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2025-03-04T20:59:01.0157993Z         >>>                         sync_bn_network,
2025-03-04T20:59:01.0158373Z         >>>                         device_ids=[args.local_rank],
2025-03-04T20:59:01.0158767Z         >>>                         output_device=args.local_rank)
2025-03-04T20:59:01.0159120Z     
2025-03-04T20:59:01.0159510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.0159898Z 
2025-03-04T20:59:01.0160565Z 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=825.
2025-03-04T20:59:01.0161652Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.0162362Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2025-03-04T20:59:01.0162805Z 
2025-03-04T20:59:01.0162902Z         Args:
2025-03-04T20:59:01.0163303Z             module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2025-03-04T20:59:01.0163909Z             process_group (optional): process group to scope synchronization,
2025-03-04T20:59:01.0164384Z                 default is the whole world
2025-03-04T20:59:01.0164613Z 
2025-03-04T20:59:01.0164727Z         Returns:
2025-03-04T20:59:01.0165146Z             The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2025-03-04T20:59:01.0165763Z             layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2025-03-04T20:59:01.0166335Z             a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2025-03-04T20:59:01.0166778Z             instead.
2025-03-04T20:59:01.0166933Z 
2025-03-04T20:59:01.0167046Z         Example::
2025-03-04T20:59:01.0167196Z 
2025-03-04T20:59:01.0167340Z             >>> # Network with nn.BatchNorm layer
2025-03-04T20:59:01.0167744Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:01.0168176Z             >>> module = torch.nn.Sequential(
2025-03-04T20:59:01.0168551Z             >>>            torch.nn.Linear(20, 100),
2025-03-04T20:59:01.0168921Z             >>>            torch.nn.BatchNorm1d(100),
2025-03-04T20:59:01.0169276Z             >>>          ).cuda()
2025-03-04T20:59:01.0169627Z             >>> # creating process group (optional)
2025-03-04T20:59:01.0170040Z             >>> # ranks is a list of int identifying rank ids.
2025-03-04T20:59:01.0170428Z             >>> ranks = list(range(8))
2025-03-04T20:59:01.0170771Z             >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-04T20:59:01.0171166Z             >>> # Note: every rank calls into new_group for every
2025-03-04T20:59:01.0171612Z             >>> # process group created, even if that rank is not
2025-03-04T20:59:01.0172009Z             >>> # part of the group.
2025-03-04T20:59:01.0172362Z             >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:01.0172871Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-04T20:59:01.0173475Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-04T20:59:01.0174429Z             >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2025-03-04T20:59:01.0174858Z 
2025-03-04T20:59:01.0175043Z         
2025-03-04T20:59:01.0175449Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.0175829Z 
2025-03-04T20:59:01.0370578Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60.
2025-03-04T20:59:01.0371538Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.0371929Z 
2025-03-04T20:59:01.0372261Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2025-03-04T20:59:01.0372710Z 
2025-03-04T20:59:01.0372995Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2025-03-04T20:59:01.0373806Z   be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2025-03-04T20:59:01.0374194Z 
2025-03-04T20:59:01.0374557Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2025-03-04T20:59:01.0375279Z   a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2025-03-04T20:59:01.0375840Z   (tuple of `(name, size)` tuples) for `NamedTensor` input.
2025-03-04T20:59:01.0376143Z 
2025-03-04T20:59:01.0376234Z Shape:
2025-03-04T20:59:01.0376600Z     - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2025-03-04T20:59:01.0394899Z       dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2025-03-04T20:59:01.0395646Z     - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2025-03-04T20:59:01.0396155Z       :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2025-03-04T20:59:01.0396423Z 
2025-03-04T20:59:01.0396518Z Args:
2025-03-04T20:59:01.0396816Z     dim (Union[int, str]): Dimension to be unflattened
2025-03-04T20:59:01.0397452Z     unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2025-03-04T20:59:01.0397934Z 
2025-03-04T20:59:01.0398035Z Examples:
2025-03-04T20:59:01.0398288Z     >>> input = torch.randn(2, 50)
2025-03-04T20:59:01.0398615Z     >>> # With tuple of ints
2025-03-04T20:59:01.0398919Z     >>> m = nn.Sequential(
2025-03-04T20:59:01.0399212Z     >>>     nn.Linear(50, 50),
2025-03-04T20:59:01.0399523Z     >>>     nn.Unflatten(1, (2, 5, 5))
2025-03-04T20:59:01.0399842Z     >>> )
2025-03-04T20:59:01.0400083Z     >>> output = m(input)
2025-03-04T20:59:01.0400380Z     >>> output.size()
2025-03-04T20:59:01.0400659Z     torch.Size([2, 2, 5, 5])
2025-03-04T20:59:01.0400959Z     >>> # With torch.Size
2025-03-04T20:59:01.0401235Z     >>> m = nn.Sequential(
2025-03-04T20:59:01.0401583Z     >>>     nn.Linear(50, 50),
2025-03-04T20:59:01.0401912Z     >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2025-03-04T20:59:01.0402265Z     >>> )
2025-03-04T20:59:01.0402508Z     >>> output = m(input)
2025-03-04T20:59:01.0402796Z     >>> output.size()
2025-03-04T20:59:01.0403076Z     torch.Size([2, 2, 5, 5])
2025-03-04T20:59:01.0403399Z     >>> # With namedshape (tuple of tuples)
2025-03-04T20:59:01.0403804Z     >>> input = torch.randn(2, 50, names=('N', 'features'))
2025-03-04T20:59:01.0404299Z     >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
2025-03-04T20:59:01.0404755Z     >>> output = unflatten(input)
2025-03-04T20:59:01.0405081Z     >>> output.size()
2025-03-04T20:59:01.0405361Z     torch.Size([2, 2, 5, 5])
2025-03-04T20:59:01.0405549Z 
2025-03-04T20:59:01.0405820Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.0406200Z 
2025-03-04T20:59:01.0703301Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1700.
2025-03-04T20:59:01.0704500Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.0705145Z Creates a criterion that measures the triplet loss given input
2025-03-04T20:59:01.0705745Z     tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2025-03-04T20:59:01.0706300Z     positive, and negative examples, respectively), and a nonnegative,
2025-03-04T20:59:01.0706907Z     real-valued function ("distance function") used to compute the relationship
2025-03-04T20:59:01.0707522Z     between the anchor and positive example ("positive distance") and the
2025-03-04T20:59:01.0708048Z     anchor and negative example ("negative distance").
2025-03-04T20:59:01.0708327Z 
2025-03-04T20:59:01.0708559Z     The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2025-03-04T20:59:01.0709005Z     can be described as:
2025-03-04T20:59:01.0709185Z 
2025-03-04T20:59:01.0709311Z     .. math::
2025-03-04T20:59:01.0709609Z         \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2025-03-04T20:59:01.0710048Z         l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2025-03-04T20:59:01.0710322Z 
2025-03-04T20:59:01.0710590Z     where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2025-03-04T20:59:01.0711276Z     quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2025-03-04T20:59:01.0711957Z     and :math:`margin` is a nonnegative margin representing the minimum difference
2025-03-04T20:59:01.0712586Z     between the positive and negative distances that is required for the loss to
2025-03-04T20:59:01.0713265Z     be 0.  The input tensors have :math:`N` elements each and can be of any shape
2025-03-04T20:59:01.0713758Z     that the distance function can handle.
2025-03-04T20:59:01.0713990Z 
2025-03-04T20:59:01.0714129Z     If :attr:`reduction` is not ``'none'``
2025-03-04T20:59:01.0714473Z     (default ``'mean'``), then:
2025-03-04T20:59:01.0714663Z 
2025-03-04T20:59:01.0714776Z     .. math::
2025-03-04T20:59:01.0715019Z         \ell(x, y) =
2025-03-04T20:59:01.0715279Z         \begin{cases}
2025-03-04T20:59:01.0715646Z             \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2025-03-04T20:59:01.0716187Z             \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2025-03-04T20:59:01.0716606Z         \end{cases}
2025-03-04T20:59:01.0716770Z 
2025-03-04T20:59:01.0717013Z     See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2025-03-04T20:59:01.0717641Z     loss for input tensors using the :math:`l_p` distance as the distance function.
2025-03-04T20:59:01.0718033Z 
2025-03-04T20:59:01.0718129Z     Args:
2025-03-04T20:59:01.0718549Z         distance_function (Callable, optional): A nonnegative, real-valued function that
2025-03-04T20:59:01.0719159Z             quantifies the closeness of two tensors. If not specified,
2025-03-04T20:59:01.0719711Z             `nn.PairwiseDistance` will be used.  Default: ``None``
2025-03-04T20:59:01.0720287Z         margin (float, optional): A nonnegative margin representing the minimum difference
2025-03-04T20:59:01.0720975Z             between the positive and negative distances required for the loss to be 0. Larger
2025-03-04T20:59:01.0721670Z             margins penalize cases where the negative examples are not distant enough from the
2025-03-04T20:59:01.0722270Z             anchors, relative to the positives. Default: :math:`1`.
2025-03-04T20:59:01.0722831Z         swap (bool, optional): Whether to use the distance swap described in the paper
2025-03-04T20:59:01.0723484Z             `Learning shallow convolutional feature descriptors with triplet losses` by
2025-03-04T20:59:01.0724123Z             V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2025-03-04T20:59:01.0724765Z             negative example than the anchor is, swaps the positive example and the anchor in
2025-03-04T20:59:01.0725313Z             the loss computation. Default: ``False``.
2025-03-04T20:59:01.0725860Z         reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2025-03-04T20:59:01.0726503Z             ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-03-04T20:59:01.0727050Z             ``'mean'``: the sum of the output will be divided by the number of
2025-03-04T20:59:01.0727612Z             elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2025-03-04T20:59:01.0727978Z 
2025-03-04T20:59:01.0727982Z 
2025-03-04T20:59:01.0728090Z     Shape:
2025-03-04T20:59:01.0728482Z         - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2025-03-04T20:59:01.0728997Z           as supported by the distance function.
2025-03-04T20:59:01.0729514Z         - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2025-03-04T20:59:01.0730004Z           otherwise.
2025-03-04T20:59:01.0730166Z 
2025-03-04T20:59:01.0730280Z     Examples::
2025-03-04T20:59:01.0730423Z 
2025-03-04T20:59:01.0730549Z     >>> # Initialize embeddings
2025-03-04T20:59:01.0730876Z     >>> embedding = nn.Embedding(1000, 128)
2025-03-04T20:59:01.0731248Z     >>> anchor_ids = torch.randint(0, 1000, (1,))
2025-03-04T20:59:01.0731639Z     >>> positive_ids = torch.randint(0, 1000, (1,))
2025-03-04T20:59:01.0732022Z     >>> negative_ids = torch.randint(0, 1000, (1,))
2025-03-04T20:59:01.0732395Z     >>> anchor = embedding(anchor_ids)
2025-03-04T20:59:01.0732751Z     >>> positive = embedding(positive_ids)
2025-03-04T20:59:01.0733114Z     >>> negative = embedding(negative_ids)
2025-03-04T20:59:01.0733445Z     >>>
2025-03-04T20:59:01.0733721Z     >>> # Built-in Distance Function
2025-03-04T20:59:01.0734051Z     >>> triplet_loss = \
2025-03-04T20:59:01.0734518Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2025-03-04T20:59:01.0735096Z     >>> output = triplet_loss(anchor, positive, negative)
2025-03-04T20:59:01.0735485Z     >>> output.backward()
2025-03-04T20:59:01.0735763Z     >>>
2025-03-04T20:59:01.0736009Z     >>> # Custom Distance Function
2025-03-04T20:59:01.0736336Z     >>> def l_infinity(x1, x2):
2025-03-04T20:59:01.0736701Z     >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2025-03-04T20:59:01.0737074Z     >>>
2025-03-04T20:59:01.0737408Z     >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2025-03-04T20:59:01.0737922Z     >>> triplet_loss = (
2025-03-04T20:59:01.0738384Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2025-03-04T20:59:01.0738965Z     >>> output = triplet_loss(anchor, positive, negative)
2025-03-04T20:59:01.0739358Z     >>> output.backward()
2025-03-04T20:59:01.0739638Z     >>>
2025-03-04T20:59:01.0739895Z     >>> # Custom Distance Function (Lambda)
2025-03-04T20:59:01.0740236Z     >>> triplet_loss = (
2025-03-04T20:59:01.0740554Z     >>>     nn.TripletMarginWithDistanceLoss(
2025-03-04T20:59:01.0741088Z     >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2025-03-04T20:59:01.0741603Z     >>> output = triplet_loss(anchor, positive, negative)
2025-03-04T20:59:01.0741996Z     >>> output.backward()
2025-03-04T20:59:01.0742175Z 
2025-03-04T20:59:01.0742289Z     Reference:
2025-03-04T20:59:01.0742761Z         V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2025-03-04T20:59:01.0743441Z         https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html
2025-03-04T20:59:01.0743883Z     
2025-03-04T20:59:01.0744270Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2025-03-04T20:59:01.0744677Z 
2025-03-04T20:59:01.1279172Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395.
2025-03-04T20:59:01.1280131Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.1280705Z Computes a partial inverse of :class:`MaxPool2d`.
2025-03-04T20:59:01.1280991Z 
2025-03-04T20:59:01.1281451Z     :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2025-03-04T20:59:01.1281858Z 
2025-03-04T20:59:01.1282095Z     :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2025-03-04T20:59:01.1282778Z     including the indices of the maximal values and computes a partial inverse
2025-03-04T20:59:01.1283309Z     in which all non-maximal values are set to zero.
2025-03-04T20:59:01.1283589Z 
2025-03-04T20:59:01.1283685Z     Note:
2025-03-04T20:59:01.1284156Z         This operation may behave nondeterministically when the input indices has repeat values.
2025-03-04T20:59:01.1285010Z         See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2025-03-04T20:59:01.1285526Z 
2025-03-04T20:59:01.1285786Z     .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2025-03-04T20:59:01.1286339Z               sizes. Hence, the inversion process can get ambiguous.
2025-03-04T20:59:01.1286850Z               To accommodate this, you can provide the needed output size
2025-03-04T20:59:01.1287396Z               as an additional argument :attr:`output_size` in the forward call.
2025-03-04T20:59:01.1287868Z               See the Inputs and Example below.
2025-03-04T20:59:01.1288214Z 
2025-03-04T20:59:01.1288307Z     Args:
2025-03-04T20:59:01.1288638Z         kernel_size (int or tuple): Size of the max pooling window.
2025-03-04T20:59:01.1289138Z         stride (int or tuple): Stride of the max pooling window.
2025-03-04T20:59:01.1289582Z             It is set to :attr:`kernel_size` by default.
2025-03-04T20:59:01.1290098Z         padding (int or tuple): Padding that was added to the input
2025-03-04T20:59:01.1290448Z 
2025-03-04T20:59:01.1290543Z     Inputs:
2025-03-04T20:59:01.1290811Z         - `input`: the input Tensor to invert
2025-03-04T20:59:01.1291266Z         - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2025-03-04T20:59:01.1291771Z         - `output_size` (optional): the targeted output size
2025-03-04T20:59:01.1292064Z 
2025-03-04T20:59:01.1292156Z     Shape:
2025-03-04T20:59:01.1292489Z         - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2025-03-04T20:59:01.1293027Z         - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2025-03-04T20:59:01.1293368Z 
2025-03-04T20:59:01.1293482Z           .. math::
2025-03-04T20:59:01.1293915Z             H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2025-03-04T20:59:01.1294306Z 
2025-03-04T20:59:01.1294415Z           .. math::
2025-03-04T20:59:01.1294841Z             W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2025-03-04T20:59:01.1295226Z 
2025-03-04T20:59:01.1295410Z           or as given by :attr:`output_size` in the call operator
2025-03-04T20:59:01.1295749Z 
2025-03-04T20:59:01.1295863Z     Example::
2025-03-04T20:59:01.1296003Z 
2025-03-04T20:59:01.1296189Z         >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2025-03-04T20:59:01.1296620Z         >>> unpool = nn.MaxUnpool2d(2, stride=2)
2025-03-04T20:59:01.1297009Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2025-03-04T20:59:01.1297390Z                                     [ 5.,  6.,  7.,  8.],
2025-03-04T20:59:01.1297806Z                                     [ 9., 10., 11., 12.],
2025-03-04T20:59:01.1298170Z                                     [13., 14., 15., 16.]]]])
2025-03-04T20:59:01.1298538Z         >>> output, indices = pool(input)
2025-03-04T20:59:01.1298891Z         >>> unpool(output, indices)
2025-03-04T20:59:01.1299300Z         tensor([[[[  0.,   0.,   0.,   0.],
2025-03-04T20:59:01.1299632Z                   [  0.,   6.,   0.,   8.],
2025-03-04T20:59:01.1299961Z                   [  0.,   0.,   0.,   0.],
2025-03-04T20:59:01.1300292Z                   [  0.,  14.,   0.,  16.]]]])
2025-03-04T20:59:01.1300739Z         >>> # Now using output_size to resolve an ambiguous size for the inverse
2025-03-04T20:59:01.1301316Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2025-03-04T20:59:01.1301712Z                                     [ 6.,  7.,  8.,  9., 10.],
2025-03-04T20:59:01.1302118Z                                     [11., 12., 13., 14., 15.],
2025-03-04T20:59:01.1302510Z                                     [16., 17., 18., 19., 20.]]]])
2025-03-04T20:59:01.1302873Z         >>> output, indices = pool(input)
2025-03-04T20:59:01.1303291Z         >>> # This call will not work without specifying output_size
2025-03-04T20:59:01.1303758Z         >>> unpool(output, indices, output_size=input.size())
2025-03-04T20:59:01.1304155Z         tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2025-03-04T20:59:01.1304489Z                   [ 0.,  7.,  0.,  9.,  0.],
2025-03-04T20:59:01.1304818Z                   [ 0.,  0.,  0.,  0.,  0.],
2025-03-04T20:59:01.1305150Z                   [ 0., 17.,  0., 19.,  0.]]]])
2025-03-04T20:59:01.1305375Z 
2025-03-04T20:59:01.1305378Z 
2025-03-04T20:59:01.1305481Z     
2025-03-04T20:59:01.1305872Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.1306253Z 
2025-03-04T20:59:01.1553516Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270.
2025-03-04T20:59:01.1554461Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.1555182Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2025-03-04T20:59:01.1555621Z 
2025-03-04T20:59:01.1556153Z     For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2025-03-04T20:59:01.1556724Z     and with 2D inputs, this class
2025-03-04T20:59:01.1556934Z 
2025-03-04T20:59:01.1557265Z         * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2025-03-04T20:59:01.1558023Z         * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2025-03-04T20:59:01.1558781Z         * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2025-03-04T20:59:01.1559219Z 
2025-03-04T20:59:01.1559585Z     However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2025-03-04T20:59:01.1560174Z     operations.
2025-03-04T20:59:01.1560323Z 
2025-03-04T20:59:01.1560603Z     EmbeddingBag also supports per-sample weights as an argument to the forward
2025-03-04T20:59:01.1561237Z     pass. This scales the output of the Embedding before performing a weighted
2025-03-04T20:59:01.1561869Z     reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2025-03-04T20:59:01.1562504Z     only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2025-03-04T20:59:01.1563055Z     :attr:`per_sample_weights`.
2025-03-04T20:59:01.1563259Z 
2025-03-04T20:59:01.1563370Z     Args:
2025-03-04T20:59:01.1563704Z         num_embeddings (int): size of the dictionary of embeddings
2025-03-04T20:59:01.1564197Z         embedding_dim (int): the size of each embedding vector
2025-03-04T20:59:01.1564821Z         max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2025-03-04T20:59:01.1565429Z                                     is renormalized to have norm :attr:`max_norm`.
2025-03-04T20:59:01.1566072Z         norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2025-03-04T20:59:01.1566884Z         scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2025-03-04T20:59:01.1567515Z                                                 the words in the mini-batch. Default ``False``.
2025-03-04T20:59:01.1568005Z                                                 Note: this option is not supported when ``mode="max"``.
2025-03-04T20:59:01.1568585Z         mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2025-03-04T20:59:01.1569265Z                                  ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2025-03-04T20:59:01.1569903Z                                  into consideration. ``"mean"`` computes the average of the values
2025-03-04T20:59:01.1570443Z                                  in the bag, ``"max"`` computes the max value over each bag.
2025-03-04T20:59:01.1570882Z                                  Default: ``"mean"``
2025-03-04T20:59:01.1571443Z         sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2025-03-04T20:59:01.1572170Z                                  Notes for more details regarding sparse gradients. Note: this option is not
2025-03-04T20:59:01.1572709Z                                  supported when ``mode="max"``.
2025-03-04T20:59:01.1573366Z         include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2025-03-04T20:59:01.1574404Z                                       is equivalent to the size of `indices`. This matches the CSR format.
2025-03-04T20:59:01.1575116Z         padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2025-03-04T20:59:01.1575870Z                                      gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2025-03-04T20:59:01.1576543Z                                      during training, i.e. it remains as a fixed "pad". For a newly constructed
2025-03-04T20:59:01.1577271Z                                      EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2025-03-04T20:59:01.1577993Z                                      zeros, but can be updated to another value to be used as the padding vector.
2025-03-04T20:59:01.1578635Z                                      Note that the embedding vector at :attr:`padding_idx` is excluded from the
2025-03-04T20:59:01.1579137Z                                      reduction.
2025-03-04T20:59:01.1579379Z 
2025-03-04T20:59:01.1579480Z     Attributes:
2025-03-04T20:59:01.1579967Z         weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2025-03-04T20:59:01.1580569Z                          initialized from :math:`\mathcal{N}(0, 1)`.
2025-03-04T20:59:01.1580848Z 
2025-03-04T20:59:01.1580963Z     Examples::
2025-03-04T20:59:01.1581120Z 
2025-03-04T20:59:01.1581300Z         >>> # an EmbeddingBag module containing 10 tensors of size 3
2025-03-04T20:59:01.1581774Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2025-03-04T20:59:01.1582192Z         >>> # a batch of 2 samples of 4 indices each
2025-03-04T20:59:01.1582640Z         >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2025-03-04T20:59:01.1583168Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-04T20:59:01.1583592Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:01.1583989Z         >>> embedding_sum(input, offsets)
2025-03-04T20:59:01.1584353Z         tensor([[-0.8861, -5.4350, -0.0523],
2025-03-04T20:59:01.1584702Z                 [ 1.1306, -2.5798, -1.0044]])
2025-03-04T20:59:01.1584923Z 
2025-03-04T20:59:01.1585055Z         >>> # Example with padding_idx
2025-03-04T20:59:01.1585496Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2025-03-04T20:59:01.1586033Z         >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2025-03-04T20:59:01.1586512Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-04T20:59:01.1586915Z         >>> embedding_sum(input, offsets)
2025-03-04T20:59:01.1587265Z         tensor([[ 0.0000,  0.0000,  0.0000],
2025-03-04T20:59:01.1587605Z                 [-0.7082,  3.2145, -2.6251]])
2025-03-04T20:59:01.1587825Z 
2025-03-04T20:59:01.1588020Z         >>> # An EmbeddingBag can be loaded from an Embedding like so
2025-03-04T20:59:01.1588526Z         >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2025-03-04T20:59:01.1588969Z         >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2025-03-04T20:59:01.1589354Z                 embedding.weight,
2025-03-04T20:59:01.1589748Z                 padding_idx=embedding.padding_idx,
2025-03-04T20:59:01.1590107Z                 mode='sum')
2025-03-04T20:59:01.1590387Z     
2025-03-04T20:59:01.1590779Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.1591170Z 
2025-03-04T20:59:01.1922448Z 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=1742.
2025-03-04T20:59:01.1923638Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.1924046Z 
2025-03-04T20:59:01.1924330Z Context manager for training with uneven inputs across processes in DDP.
2025-03-04T20:59:01.1924726Z 
2025-03-04T20:59:01.1924961Z This context manager will keep track of already-joined DDP processes,
2025-03-04T20:59:01.1925596Z and "shadow" the forward and backward passes by inserting collective
2025-03-04T20:59:01.1926233Z communication operations to match with the ones created by non-joined
2025-03-04T20:59:01.1926889Z DDP processes. This will ensure each collective call has a corresponding
2025-03-04T20:59:01.1927539Z call by already-joined DDP processes, preventing hangs or errors that
2025-03-04T20:59:01.1928116Z would otherwise happen when training with uneven inputs across
2025-03-04T20:59:01.1928733Z processes. Alternatively, if the flag ``throw_on_early_termination`` is
2025-03-04T20:59:01.1929496Z specified to be ``True``, all trainers will throw an error once one rank
2025-03-04T20:59:01.1930111Z runs out of inputs, allowing these errors to be caught and handled
2025-03-04T20:59:01.1930615Z according to application logic.
2025-03-04T20:59:01.1930838Z 
2025-03-04T20:59:01.1931064Z Once all DDP processes have joined, the context manager will broadcast
2025-03-04T20:59:01.1931712Z the model corresponding to the last joined process to all processes to
2025-03-04T20:59:01.1932285Z ensure the model is the same across all processes
2025-03-04T20:59:01.1932683Z (which is guaranteed by DDP).
2025-03-04T20:59:01.1932965Z 
2025-03-04T20:59:01.1933205Z To use this to enable training with uneven inputs across processes,
2025-03-04T20:59:01.1933817Z simply wrap this context manager around your training loop. No further
2025-03-04T20:59:01.1934404Z modifications to the model or data loading is required.
2025-03-04T20:59:01.1934697Z 
2025-03-04T20:59:01.1934826Z .. warning::
2025-03-04T20:59:01.1935247Z     If the model or training loop this context manager is wrapped around
2025-03-04T20:59:01.1935795Z     has additional distributed collective operations, such as
2025-03-04T20:59:01.1936335Z     ``SyncBatchNorm`` in the model's forward pass, then the flag
2025-03-04T20:59:01.1937004Z     ``throw_on_early_termination`` must be enabled. This is because this
2025-03-04T20:59:01.1937645Z     context manager is not aware of non-DDP collective communication.
2025-03-04T20:59:01.1938289Z     This flag will cause all ranks to throw when any one rank
2025-03-04T20:59:01.1938817Z     exhausts inputs, allowing these errors to be caught and recovered
2025-03-04T20:59:01.1939336Z     from across all ranks.
2025-03-04T20:59:01.1939519Z 
2025-03-04T20:59:01.1939646Z Args:
2025-03-04T20:59:01.1940003Z     divide_by_initial_world_size (bool): If ``True``, will divide
2025-03-04T20:59:01.1940592Z         gradients by the initial ``world_size`` DDP training was launched
2025-03-04T20:59:01.1941110Z         with. If ``False``, will compute the effective world size
2025-03-04T20:59:01.1941665Z         (number of ranks that have not depleted their inputs yet) and
2025-03-04T20:59:01.1942182Z         divide gradients by that during allreduce. Set
2025-03-04T20:59:01.1942645Z         ``divide_by_initial_world_size=True`` to ensure every input
2025-03-04T20:59:01.1943226Z         sample including the uneven inputs have equal weight in terms of
2025-03-04T20:59:01.1943883Z         how much they contribute to the global gradient. This is
2025-03-04T20:59:01.1944476Z         achieved by always dividing the gradient by the initial
2025-03-04T20:59:01.1945003Z         ``world_size`` even when we encounter uneven inputs. If you set
2025-03-04T20:59:01.1945536Z         this to ``False``, we divide the gradient by the remaining
2025-03-04T20:59:01.1946115Z         number of nodes. This ensures parity with training on a smaller
2025-03-04T20:59:01.1946697Z         ``world_size`` although it also means the uneven inputs would
2025-03-04T20:59:01.1947222Z         contribute more towards the global gradient. Typically, you
2025-03-04T20:59:01.1947805Z         would want to set this to ``True`` for cases where the last few
2025-03-04T20:59:01.1948385Z         inputs of your training job are uneven. In extreme cases, where
2025-03-04T20:59:01.1949164Z         there is a large discrepancy in the number of inputs, setting
2025-03-04T20:59:01.1949790Z         this to ``False`` might provide better results.
2025-03-04T20:59:01.1950295Z     enable (bool): Whether to enable uneven input detection or not. Pass
2025-03-04T20:59:01.1951165Z         in ``enable=False`` to disable in cases where you know that
2025-03-04T20:59:01.1952076Z         inputs are even across participating processes. Default is
2025-03-04T20:59:01.1952825Z         ``True``.
2025-03-04T20:59:01.1953229Z     throw_on_early_termination (bool): Whether to throw an error
2025-03-04T20:59:01.1953732Z         or continue training when at least one rank has exhausted
2025-03-04T20:59:01.1954308Z         inputs. If ``True``, will throw upon the first rank reaching end
2025-03-04T20:59:01.1954810Z         of data. If ``False``, will continue training with a smaller
2025-03-04T20:59:01.1955318Z         effective world size until all ranks are joined. Note that if
2025-03-04T20:59:01.1955778Z         this flag is specified, then the flag
2025-03-04T20:59:01.1956208Z         ``divide_by_initial_world_size`` would be ignored. Default
2025-03-04T20:59:01.1956596Z         is ``False``.
2025-03-04T20:59:01.1956771Z 
2025-03-04T20:59:01.1956776Z 
2025-03-04T20:59:01.1956886Z Example::
2025-03-04T20:59:01.1957031Z 
2025-03-04T20:59:01.1957153Z     >>> # xdoctest: +SKIP("Distributed")
2025-03-04T20:59:01.1957490Z     >>> import torch
2025-03-04T20:59:01.1957781Z     >>> import torch.distributed as dist
2025-03-04T20:59:01.1958141Z     >>> import os
2025-03-04T20:59:01.1958427Z     >>> import torch.multiprocessing as mp
2025-03-04T20:59:01.1958783Z     >>> import torch.nn as nn
2025-03-04T20:59:01.1959098Z     >>> # On each spawned worker
2025-03-04T20:59:01.1959406Z     >>> def worker(rank):
2025-03-04T20:59:01.1959772Z     >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2025-03-04T20:59:01.1960272Z     >>>     torch.cuda.set_device(rank)
2025-03-04T20:59:01.1960652Z     >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2025-03-04T20:59:01.1961098Z     >>>     model = torch.nn.parallel.DistributedDataParallel(
2025-03-04T20:59:01.1961552Z     >>>         model, device_ids=[rank], output_device=rank
2025-03-04T20:59:01.1961918Z     >>>     )
2025-03-04T20:59:01.1962202Z     >>>     # Rank 1 gets one more input than rank 0.
2025-03-04T20:59:01.1962650Z     >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2025-03-04T20:59:01.1963081Z     >>>     with model.join():
2025-03-04T20:59:01.1963374Z     >>>         for _ in range(5):
2025-03-04T20:59:01.1963695Z     >>>             for inp in inputs:
2025-03-04T20:59:01.1964181Z     >>>                 loss = model(inp).sum()
2025-03-04T20:59:01.1964537Z     >>>                 loss.backward()
2025-03-04T20:59:01.1964959Z     >>>     # Without the join() API, the below synchronization will hang
2025-03-04T20:59:01.1965438Z     >>>     # blocking for rank 1's allreduce to complete.
2025-03-04T20:59:01.1965852Z     >>>     torch.cuda.synchronize(device=rank)
2025-03-04T20:59:01.1966152Z 
2025-03-04T20:59:01.1966415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.1966803Z 
2025-03-04T20:59:01.1967603Z 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=2033.
2025-03-04T20:59:01.1968725Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.1969128Z 
2025-03-04T20:59:01.1969443Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2025-03-04T20:59:01.1969892Z 
2025-03-04T20:59:01.1970108Z Registers an optimizer with DDP such that the optimization for a
2025-03-04T20:59:01.1970665Z parameter will run immediately when that parameter's gradient is
2025-03-04T20:59:01.1971217Z finished with reduction, instead of waiting for all parameters'
2025-03-04T20:59:01.1971780Z gradients to finish reduction. This can result in a training speedup
2025-03-04T20:59:01.1972352Z depending on your workload since the optimizer can run while gradient
2025-03-04T20:59:01.1972939Z reduction for other parameters are still ongoing. In addition, this has
2025-03-04T20:59:01.1973541Z the potential to reduce peak memory consumption during training, as it
2025-03-04T20:59:01.1974300Z only needs to load the per-parameter optimizer states of a single
2025-03-04T20:59:01.1974852Z parameter at a time, instead of loading all per-parameter optimizer
2025-03-04T20:59:01.1975293Z states at once.
2025-03-04T20:59:01.1975456Z 
2025-03-04T20:59:01.1975547Z Args:
2025-03-04T20:59:01.1975949Z     optim (Type): a ``torch.optim.Optimizer`` class to be registered
2025-03-04T20:59:01.1976392Z     as a fused optimizer.
2025-03-04T20:59:01.1976762Z     *args (Sequence[Any]): Arguments to forward to `optim`.
2025-03-04T20:59:01.1977282Z     optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2025-03-04T20:59:01.1977929Z     to optimize, similar to `params` argument of traditional `torch.optim`
2025-03-04T20:59:01.1978502Z     Optimizers. If this is omitted, all DDP model parameters will be
2025-03-04T20:59:01.1978936Z     optimized.
2025-03-04T20:59:01.1979299Z     **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2025-03-04T20:59:01.1979618Z 
2025-03-04T20:59:01.1979730Z .. warning ::
2025-03-04T20:59:01.1980101Z     _register_fused_optim should only be called once on a DDP instance,
2025-03-04T20:59:01.1980662Z     and registering multiple fused optimizers for the same DDP model
2025-03-04T20:59:01.1981125Z     is not currently supported. Please ping
2025-03-04T20:59:01.1981626Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-04T20:59:01.1982089Z     for your use case.
2025-03-04T20:59:01.1982357Z 
2025-03-04T20:59:01.1982455Z .. warning ::
2025-03-04T20:59:01.1982807Z     _register_fused_optim and register_comm_hook currently do not
2025-03-04T20:59:01.1983421Z     compose together, meaning that custom DDP communication hooks are
2025-03-04T20:59:01.1983953Z     not supported with overlapped optimizers. Please ping
2025-03-04T20:59:01.1984489Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-04T20:59:01.1984953Z     for your use case.
2025-03-04T20:59:01.1985137Z 
2025-03-04T20:59:01.1985235Z .. warning ::
2025-03-04T20:59:01.1985617Z     Gradient accumulation and DDP `no_sync` are currently not supported
2025-03-04T20:59:01.1986106Z     with overlapped optimizer. Please ping
2025-03-04T20:59:01.1986589Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-04T20:59:01.1987048Z     for your use case.
2025-03-04T20:59:01.1987216Z 
2025-03-04T20:59:01.1987325Z Example::
2025-03-04T20:59:01.1987454Z 
2025-03-04T20:59:01.1987606Z     >>> # xdoctest: +SKIP("No rendezvous handler")
2025-03-04T20:59:01.1988172Z     >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2025-03-04T20:59:01.1988821Z     >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2025-03-04T20:59:01.1989296Z     >>> lr = 1e-2
2025-03-04T20:59:01.1989558Z     >>> betas = (0.9, 0.99)
2025-03-04T20:59:01.1989897Z     >>> eps = 1e-6
2025-03-04T20:59:01.1990332Z     >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2025-03-04T20:59:01.1990810Z     >>> # Example with subset of parameters
2025-03-04T20:59:01.1991205Z     >>> params_to_opt = [list(net.parameters())[0]]
2025-03-04T20:59:01.1991590Z     >>> net._register_fused_optim(
2025-03-04T20:59:01.1992050Z     ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2025-03-04T20:59:01.1992514Z     ... )
2025-03-04T20:59:01.1992660Z 
2025-03-04T20:59:01.1992923Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.1993315Z 
2025-03-04T20:59:01.2449712Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6.
2025-03-04T20:59:01.2450779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.2451405Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2025-03-04T20:59:01.2451766Z 
2025-03-04T20:59:01.2452055Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-04T20:59:01.2452747Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-04T20:59:01.2453423Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-04T20:59:01.2454240Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-04T20:59:01.2454850Z 
2025-03-04T20:59:01.2454991Z     .. note::
2025-03-04T20:59:01.2455506Z         Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2025-03-04T20:59:01.2456518Z         than the utility function ``convert_conv2d_weight_memory_format``. Any
2025-03-04T20:59:01.2457098Z         layer with 4d weight will be affected by ``model.to``, which does not
2025-03-04T20:59:01.2457682Z         necessarily benefit from conversion to specified ``memory_format``.
2025-03-04T20:59:01.2458348Z         One place we are confident in is that NHWC(channels_last) conversion for
2025-03-04T20:59:01.2458935Z         convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2025-03-04T20:59:01.2459501Z         even in cases where we have to apply permutation to input tensors.
2025-03-04T20:59:01.2459845Z 
2025-03-04T20:59:01.2460081Z         Hence our strategy here is to convert only the weight of convolution to
2025-03-04T20:59:01.2460571Z         channels_last. This ensures that;
2025-03-04T20:59:01.2461045Z         1. Fast convolution kernels will be used, the benefit of which could
2025-03-04T20:59:01.2461639Z         outweigh overhead of permutation (if input is not in the same format).
2025-03-04T20:59:01.2462346Z         2. No unnecessary permutations are applied on layers that do not benefit
2025-03-04T20:59:01.2462838Z         from memory_format conversion.
2025-03-04T20:59:01.2463074Z 
2025-03-04T20:59:01.2463418Z         The optimal case is that, layers between convolution layers are channels
2025-03-04T20:59:01.2464026Z         last compatible. Input tensor would be permuted to channels last when it
2025-03-04T20:59:01.2464640Z         encounters the first convolution layer and stay in that memory format.
2025-03-04T20:59:01.2465254Z         Hence following convolutions will not need to permute its input tensor.
2025-03-04T20:59:01.2465619Z 
2025-03-04T20:59:01.2465865Z         In case where a channels last incompatible layer is between convolution
2025-03-04T20:59:01.2466442Z         layers, we need to permute the input tensor back to contiguous format
2025-03-04T20:59:01.2467023Z         for that layer. The input tensor will go through the remaining layers in
2025-03-04T20:59:01.2467621Z         contiguous format and be permuted to channels last when it encounters
2025-03-04T20:59:01.2468264Z         another convolution layer. There's no point in propagating that
2025-03-04T20:59:01.2468839Z         permutation to an earlier layer, as most layers are quite agnostic to
2025-03-04T20:59:01.2469354Z         ``memory_format``.
2025-03-04T20:59:01.2469538Z 
2025-03-04T20:59:01.2469795Z         This claim might change when PyTorch supports fusion of permutation, as
2025-03-04T20:59:01.2470389Z         there might have been a better spot to fuse the permutation other than
2025-03-04T20:59:01.2470873Z         immediately before a convolution.
2025-03-04T20:59:01.2471108Z 
2025-03-04T20:59:01.2471245Z     Args:
2025-03-04T20:59:01.2471608Z         module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2025-03-04T20:59:01.2472069Z                             ``nn.Module``
2025-03-04T20:59:01.2472459Z         memory_format: user specified ``memory_format``,
2025-03-04T20:59:01.2472930Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-04T20:59:01.2473238Z 
2025-03-04T20:59:01.2473350Z     Returns:
2025-03-04T20:59:01.2473837Z         The original module with updated ``nn.Conv2d``
2025-03-04T20:59:01.2474108Z 
2025-03-04T20:59:01.2474217Z     Example:
2025-03-04T20:59:01.2474513Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:01.2474947Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-04T20:59:01.2475460Z         >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda")
2025-03-04T20:59:01.2475937Z         >>> model = nn.Sequential(
2025-03-04T20:59:01.2476276Z         >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2025-03-04T20:59:01.2476689Z         >>> # This is identical to:
2025-03-04T20:59:01.2477160Z         >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-04T20:59:01.2477817Z         >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-04T20:59:01.2478335Z         >>> out = model(input)
2025-03-04T20:59:01.2478628Z     
2025-03-04T20:59:01.2479021Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.2479408Z 
2025-03-04T20:59:01.2480058Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81.
2025-03-04T20:59:01.2481094Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.2481711Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2025-03-04T20:59:01.2482341Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-04T20:59:01.2483034Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-04T20:59:01.2483708Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-04T20:59:01.2484459Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-04T20:59:01.2484906Z 
2025-03-04T20:59:01.2485008Z     .. note::
2025-03-04T20:59:01.2485417Z         Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2025-03-04T20:59:01.2486039Z         than the utility function ``convert_conv3d_weight_memory_format``. Any
2025-03-04T20:59:01.2486621Z         layer with 4d weight will be affected by ``model.to``, which does not
2025-03-04T20:59:01.2487205Z         necessarily benefit from conversion to specified ``memory_format``.
2025-03-04T20:59:01.2487809Z         One place we are confident in is that NDHWC(channels_last_3d) conversion for
2025-03-04T20:59:01.2488416Z         convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2025-03-04T20:59:01.2488984Z         even in cases where we have to apply permutation to input tensors.
2025-03-04T20:59:01.2489315Z 
2025-03-04T20:59:01.2489565Z         Hence our strategy here is to convert only the weight of convolution to
2025-03-04T20:59:01.2490093Z         channels_last_3d. This ensures that;
2025-03-04T20:59:01.2490562Z         1. Fast convolution kernels will be used, the benefit of which could
2025-03-04T20:59:01.2491202Z         outweigh overhead of permutation (if input is not in the same format).
2025-03-04T20:59:01.2491818Z         2. No unnecessary permutations are applied on layers that do not benefit
2025-03-04T20:59:01.2492310Z         from memory_format conversion.
2025-03-04T20:59:01.2492534Z 
2025-03-04T20:59:01.2492785Z         The optimal case is that, layers between convolution layers are channels
2025-03-04T20:59:01.2493396Z         last compatible. Input tensor would be permuted to channels last when it
2025-03-04T20:59:01.2494012Z         encounters the first convolution layer and stay in that memory format.
2025-03-04T20:59:01.2494629Z         Hence following convolutions will not need to permute its input tensor.
2025-03-04T20:59:01.2494999Z 
2025-03-04T20:59:01.2495248Z         In case where a channels last incompatible layer is between convolution
2025-03-04T20:59:01.2495820Z         layers, we need to permute the input tensor back to contiguous format
2025-03-04T20:59:01.2496407Z         for that layer. The input tensor will go through the remaining layers in
2025-03-04T20:59:01.2497009Z         contiguous format and be permuted to channels last when it encounters
2025-03-04T20:59:01.2497597Z         another convolution layer. There's no point in propagating that
2025-03-04T20:59:01.2498250Z         permutation to an earlier layer, as most layers are quite agnostic to
2025-03-04T20:59:01.2498718Z         ``memory_format``.
2025-03-04T20:59:01.2498917Z 
2025-03-04T20:59:01.2499215Z         This claim might change when PyTorch supports fusion of permutation, as
2025-03-04T20:59:01.2499815Z         there might have been a better spot to fuse the permutation other than
2025-03-04T20:59:01.2500302Z         immediately before a convolution.
2025-03-04T20:59:01.2500548Z 
2025-03-04T20:59:01.2500642Z     Args:
2025-03-04T20:59:01.2501002Z         module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2025-03-04T20:59:01.2501462Z                             ``nn.Module``
2025-03-04T20:59:01.2501851Z         memory_format: user specified ``memory_format``,
2025-03-04T20:59:01.2502316Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-04T20:59:01.2502630Z 
2025-03-04T20:59:01.2502726Z     Returns:
2025-03-04T20:59:01.2503021Z         The original module with updated ``nn.Conv3d``
2025-03-04T20:59:01.2503283Z 
2025-03-04T20:59:01.2503388Z     Example:
2025-03-04T20:59:01.2503677Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:01.2504108Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-04T20:59:01.2504629Z         >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda")
2025-03-04T20:59:01.2505149Z         >>> model = nn.Sequential(
2025-03-04T20:59:01.2505488Z         >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2025-03-04T20:59:01.2505848Z         >>> # This is identical to:
2025-03-04T20:59:01.2506330Z         >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-04T20:59:01.2507012Z         >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-04T20:59:01.2507538Z         >>> out = model(input)
2025-03-04T20:59:01.2507820Z     
2025-03-04T20:59:01.2508215Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.2508607Z 
2025-03-04T20:59:01.2688820Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=935.
2025-03-04T20:59:01.2689761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.2690398Z Prune tensor by removing random channels along the specified dimension.
2025-03-04T20:59:01.2690769Z 
2025-03-04T20:59:01.2691049Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-04T20:59:01.2691844Z     by removing the specified ``amount`` of (currently unpruned) channels
2025-03-04T20:59:01.2692473Z     along the specified ``dim`` selected at random.
2025-03-04T20:59:01.2693213Z     Modifies module in place (and also return the modified module)
2025-03-04T20:59:01.2694009Z     by:
2025-03-04T20:59:01.2694154Z 
2025-03-04T20:59:01.2694377Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:01.2694957Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:01.2695541Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:01.2696105Z        original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:01.2696548Z        ``name+'_orig'``.
2025-03-04T20:59:01.2696735Z 
2025-03-04T20:59:01.2696831Z     Args:
2025-03-04T20:59:01.2697155Z         module (nn.Module): module containing the tensor to prune
2025-03-04T20:59:01.2697657Z         name (str): parameter name within ``module`` on which pruning
2025-03-04T20:59:01.2698180Z                 will act.
2025-03-04T20:59:01.2698712Z         amount (int or float): quantity of parameters to prune.
2025-03-04T20:59:01.2699446Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-04T20:59:01.2700389Z             fraction of parameters to prune. If ``int``, it represents the
2025-03-04T20:59:01.2700876Z             absolute number of parameters to prune.
2025-03-04T20:59:01.2701347Z         dim (int): index of the dim along which we define channels to prune.
2025-03-04T20:59:01.2701673Z 
2025-03-04T20:59:01.2701879Z     Returns:
2025-03-04T20:59:01.2702259Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-04T20:59:01.2702604Z 
2025-03-04T20:59:01.2702716Z     Examples:
2025-03-04T20:59:01.2702979Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:01.2703323Z         >>> m = prune.random_structured(
2025-03-04T20:59:01.2703806Z         ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
2025-03-04T20:59:01.2704276Z         ... )
2025-03-04T20:59:01.2704782Z         >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
2025-03-04T20:59:01.2705430Z         >>> print(columns_pruned)
2025-03-04T20:59:01.2705733Z         3
2025-03-04T20:59:01.2705953Z     
2025-03-04T20:59:01.2706346Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.2706736Z 
2025-03-04T20:59:01.2707300Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=976.
2025-03-04T20:59:01.2708215Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.2708924Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2025-03-04T20:59:01.2709432Z 
2025-03-04T20:59:01.2709692Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-04T20:59:01.2710294Z     by removing the specified ``amount`` of (currently unpruned) channels
2025-03-04T20:59:01.2710831Z     along the specified ``dim`` with the lowest L\ ``n``-norm.
2025-03-04T20:59:01.2711353Z     Modifies module in place (and also return the modified module)
2025-03-04T20:59:01.2711776Z     by:
2025-03-04T20:59:01.2711902Z 
2025-03-04T20:59:01.2712129Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:01.2712699Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:01.2713266Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:01.2713829Z        original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:01.2714261Z        ``name+'_orig'``.
2025-03-04T20:59:01.2714446Z 
2025-03-04T20:59:01.2714539Z     Args:
2025-03-04T20:59:01.2714865Z         module (nn.Module): module containing the tensor to prune
2025-03-04T20:59:01.2715368Z         name (str): parameter name within ``module`` on which pruning
2025-03-04T20:59:01.2715821Z                 will act.
2025-03-04T20:59:01.2716186Z         amount (int or float): quantity of parameters to prune.
2025-03-04T20:59:01.2716709Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-04T20:59:01.2717231Z             fraction of parameters to prune. If ``int``, it represents the
2025-03-04T20:59:01.2717709Z             absolute number of parameters to prune.
2025-03-04T20:59:01.2718182Z         n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2025-03-04T20:59:01.2718681Z             entries for argument ``p`` in :func:`torch.norm`.
2025-03-04T20:59:01.2719178Z         dim (int): index of the dim along which we define channels to prune.
2025-03-04T20:59:01.2719759Z         importance_scores (torch.Tensor): tensor of importance scores (of same
2025-03-04T20:59:01.2720332Z             shape as module parameter) used to compute mask for pruning.
2025-03-04T20:59:01.2720894Z             The values in this tensor indicate the importance of the corresponding
2025-03-04T20:59:01.2721403Z             elements in the parameter being pruned.
2025-03-04T20:59:01.2721903Z             If unspecified or None, the module parameter will be used in its place.
2025-03-04T20:59:01.2722260Z 
2025-03-04T20:59:01.2722370Z     Returns:
2025-03-04T20:59:01.2722748Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-04T20:59:01.2723094Z 
2025-03-04T20:59:01.2723207Z     Examples:
2025-03-04T20:59:01.2723485Z         >>> from torch.nn.utils import prune
2025-03-04T20:59:01.2723876Z         >>> m = prune.ln_structured(
2025-03-04T20:59:01.2724288Z         ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
2025-03-04T20:59:01.2724692Z         ... )
2025-03-04T20:59:01.2724910Z     
2025-03-04T20:59:01.2725298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.2725685Z 
2025-03-04T20:59:01.2726236Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1023.
2025-03-04T20:59:01.2727178Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.2727577Z 
2025-03-04T20:59:01.2728014Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2025-03-04T20:59:01.2728585Z 
2025-03-04T20:59:01.2728707Z Modifies modules in place by:
2025-03-04T20:59:01.2728926Z 
2025-03-04T20:59:01.2729147Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:01.2729716Z    binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:01.2730280Z 2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:01.2730836Z    original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:01.2731309Z    ``name+'_orig'``.
2025-03-04T20:59:01.2731486Z 
2025-03-04T20:59:01.2731579Z Args:
2025-03-04T20:59:01.2731926Z     parameters (Iterable of (module, name) tuples): parameters of
2025-03-04T20:59:01.2732457Z         the model to prune in a global fashion, i.e. by aggregating all
2025-03-04T20:59:01.2733001Z         weights prior to deciding which ones to prune. module must be of
2025-03-04T20:59:01.2733501Z         type :class:`nn.Module`, and name must be a string.
2025-03-04T20:59:01.2734010Z     pruning_method (function): a valid pruning function from this module,
2025-03-04T20:59:01.2734548Z         or a custom one implemented by the user that satisfies the
2025-03-04T20:59:01.2735092Z         implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2025-03-04T20:59:01.2735693Z     importance_scores (dict): a dictionary mapping (module, name) tuples to
2025-03-04T20:59:01.2736288Z         the corresponding parameter's importance scores tensor. The tensor
2025-03-04T20:59:01.2736871Z         should be the same shape as the parameter, and is used for computing
2025-03-04T20:59:01.2737352Z         mask for pruning.
2025-03-04T20:59:01.2737842Z         If unspecified or None, the parameter will be used in place of its
2025-03-04T20:59:01.2738299Z         importance scores.
2025-03-04T20:59:01.2738667Z     kwargs: other keyword arguments such as:
2025-03-04T20:59:01.2739127Z         amount (int or float): quantity of parameters to prune across the
2025-03-04T20:59:01.2739572Z         specified parameters.
2025-03-04T20:59:01.2739959Z         If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-04T20:59:01.2740464Z         fraction of parameters to prune. If ``int``, it represents the
2025-03-04T20:59:01.2740939Z         absolute number of parameters to prune.
2025-03-04T20:59:01.2741200Z 
2025-03-04T20:59:01.2741291Z Raises:
2025-03-04T20:59:01.2741584Z     TypeError: if ``PRUNING_TYPE != 'unstructured'``
2025-03-04T20:59:01.2741864Z 
2025-03-04T20:59:01.2741954Z Note:
2025-03-04T20:59:01.2742306Z     Since global structured pruning doesn't make much sense unless the
2025-03-04T20:59:01.2742861Z     norm is normalized by the size of the parameter, we now limit the
2025-03-04T20:59:01.2743352Z     scope of global pruning to unstructured methods.
2025-03-04T20:59:01.2743636Z 
2025-03-04T20:59:01.2743733Z Examples:
2025-03-04T20:59:01.2743997Z     >>> from torch.nn.utils import prune
2025-03-04T20:59:01.2744363Z     >>> from collections import OrderedDict
2025-03-04T20:59:01.2744727Z     >>> net = nn.Sequential(OrderedDict([
2025-03-04T20:59:01.2745077Z     ...     ('first', nn.Linear(10, 4)),
2025-03-04T20:59:01.2745414Z     ...     ('second', nn.Linear(4, 1)),
2025-03-04T20:59:01.2745765Z     ... ]))
2025-03-04T20:59:01.2746014Z     >>> parameters_to_prune = (
2025-03-04T20:59:01.2746332Z     ...     (net.first, 'weight'),
2025-03-04T20:59:01.2746653Z     ...     (net.second, 'weight'),
2025-03-04T20:59:01.2746960Z     ... )
2025-03-04T20:59:01.2747201Z     >>> prune.global_unstructured(
2025-03-04T20:59:01.2747535Z     ...     parameters_to_prune,
2025-03-04T20:59:01.2747886Z     ...     pruning_method=prune.L1Unstructured,
2025-03-04T20:59:01.2748239Z     ...     amount=10,
2025-03-04T20:59:01.2748502Z     ... )
2025-03-04T20:59:01.2748866Z     >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2025-03-04T20:59:01.2749322Z     tensor(10)
2025-03-04T20:59:01.2749476Z 
2025-03-04T20:59:01.2749480Z 
2025-03-04T20:59:01.2749742Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.2750134Z 
2025-03-04T20:59:01.2750664Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1142.
2025-03-04T20:59:01.2751580Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.2752377Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2025-03-04T20:59:01.2752934Z 
2025-03-04T20:59:01.2753156Z     Modifies module in place (and also return the modified module) by:
2025-03-04T20:59:01.2753507Z 
2025-03-04T20:59:01.2753726Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:01.2754298Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:01.2754865Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:01.2755428Z        original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:01.2755876Z        ``name+'_orig'``.
2025-03-04T20:59:01.2756077Z 
2025-03-04T20:59:01.2756169Z     Args:
2025-03-04T20:59:01.2756497Z         module (nn.Module): module containing the tensor to prune
2025-03-04T20:59:01.2757006Z         name (str): parameter name within ``module`` on which pruning
2025-03-04T20:59:01.2757420Z             will act.
2025-03-04T20:59:01.2757781Z         mask (Tensor): binary mask to be applied to the parameter.
2025-03-04T20:59:01.2758081Z 
2025-03-04T20:59:01.2758224Z     Returns:
2025-03-04T20:59:01.2758602Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-04T20:59:01.2758950Z 
2025-03-04T20:59:01.2759064Z     Examples:
2025-03-04T20:59:01.2759399Z         >>> from torch.nn.utils import prune
2025-03-04T20:59:01.2759768Z         >>> m = prune.custom_from_mask(
2025-03-04T20:59:01.2760185Z         ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
2025-03-04T20:59:01.2760605Z         ... )
2025-03-04T20:59:01.2760865Z         >>> print(m.bias_mask)
2025-03-04T20:59:01.2761172Z         tensor([0., 1., 0.])
2025-03-04T20:59:01.2761370Z 
2025-03-04T20:59:01.2761463Z     
2025-03-04T20:59:01.2761860Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.2762251Z 
2025-03-04T20:59:01.3944113Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=117.
2025-03-04T20:59:01.3945085Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.3945865Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2025-03-04T20:59:01.3946370Z 
2025-03-04T20:59:01.3946634Z     Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2025-03-04T20:59:01.3947285Z     Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2025-03-04T20:59:01.3947856Z     Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2025-03-04T20:59:01.3948293Z     (UAI 2018).
2025-03-04T20:59:01.3948451Z 
2025-03-04T20:59:01.3948856Z     Exponential Moving Average is a variation of `Polyak averaging`_,
2025-03-04T20:59:01.3949456Z     but using exponential weights instead of equal weights across iterations.
2025-03-04T20:59:01.3949836Z 
2025-03-04T20:59:01.3950084Z     AveragedModel class creates a copy of the provided module :attr:`model`
2025-03-04T20:59:01.3950693Z     on the device :attr:`device` and allows to compute running averages of the
2025-03-04T20:59:01.3951175Z     parameters of the :attr:`model`.
2025-03-04T20:59:01.3951406Z 
2025-03-04T20:59:01.3951501Z     Args:
2025-03-04T20:59:01.3951802Z         model (torch.nn.Module): model to use with SWA/EMA
2025-03-04T20:59:01.3952413Z         device (torch.device, optional): if provided, the averaged model will be
2025-03-04T20:59:01.3953109Z             stored on the :attr:`device`
2025-03-04T20:59:01.3953562Z         avg_fn (function, optional): the averaging function used to update
2025-03-04T20:59:01.3954449Z             parameters; the function must take in the current value of the
2025-03-04T20:59:01.3955125Z             :class:`AveragedModel` parameter, the current value of :attr:`model`
2025-03-04T20:59:01.3955685Z             parameter, and the number of models already averaged; if None,
2025-03-04T20:59:01.3956183Z             an equally weighted average is used (default: None)
2025-03-04T20:59:01.3956813Z         multi_avg_fn (function, optional): the averaging function used to update
2025-03-04T20:59:01.3957428Z             parameters inplace; the function must take in the current values of the
2025-03-04T20:59:01.3958086Z             :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2025-03-04T20:59:01.3958731Z             parameters as a list, and the number of models already averaged; if None,
2025-03-04T20:59:01.3959268Z             an equally weighted average is used (default: None)
2025-03-04T20:59:01.3959776Z         use_buffers (bool): if ``True``, it will compute running averages for
2025-03-04T20:59:01.3960354Z             both the parameters and the buffers of the model. (default: ``False``)
2025-03-04T20:59:01.3960819Z 
2025-03-04T20:59:01.3960935Z     Example:
2025-03-04T20:59:01.3961228Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:01.3961630Z         >>> loader, optimizer, model, loss_fn = ...
2025-03-04T20:59:01.3962072Z         >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2025-03-04T20:59:01.3962689Z         >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2025-03-04T20:59:01.3963175Z         >>>                                     T_max=300)
2025-03-04T20:59:01.3963529Z         >>> swa_start = 160
2025-03-04T20:59:01.3963912Z         >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2025-03-04T20:59:01.3964297Z         >>> for i in range(300):
2025-03-04T20:59:01.3964621Z         >>>      for input, target in loader:
2025-03-04T20:59:01.3964980Z         >>>          optimizer.zero_grad()
2025-03-04T20:59:01.3965363Z         >>>          loss_fn(model(input), target).backward()
2025-03-04T20:59:01.3965746Z         >>>          optimizer.step()
2025-03-04T20:59:01.3966077Z         >>>      if i > swa_start:
2025-03-04T20:59:01.3966425Z         >>>          swa_model.update_parameters(model)
2025-03-04T20:59:01.3966800Z         >>>          swa_scheduler.step()
2025-03-04T20:59:01.3967129Z         >>>      else:
2025-03-04T20:59:01.3967410Z         >>>          scheduler.step()
2025-03-04T20:59:01.3967722Z         >>>
2025-03-04T20:59:01.3968019Z         >>> # Update bn statistics for the swa_model at the end
2025-03-04T20:59:01.3968474Z         >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2025-03-04T20:59:01.3968758Z 
2025-03-04T20:59:01.3969080Z     You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2025-03-04T20:59:01.3969717Z     If no averaging function is provided, the default is to compute
2025-03-04T20:59:01.3970199Z     equally-weighted average of the weights (SWA).
2025-03-04T20:59:01.3970465Z 
2025-03-04T20:59:01.3970572Z     Example:
2025-03-04T20:59:01.3970889Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:01.3971368Z         >>> # Compute exponential moving averages of the weights and buffers
2025-03-04T20:59:01.3971890Z         >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2025-03-04T20:59:01.3972483Z         >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2025-03-04T20:59:01.3972844Z 
2025-03-04T20:59:01.3972961Z     .. note::
2025-03-04T20:59:01.3973345Z         When using SWA/EMA with models containing Batch Normalization you may
2025-03-04T20:59:01.3975625Z         need to update the activation statistics for Batch Normalization.
2025-03-04T20:59:01.3976216Z         This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2025-03-04T20:59:01.3976827Z         or by setting :attr:`use_buffers` to `True`. The first approach updates the
2025-03-04T20:59:01.3977442Z         statistics in a post-training step by passing data through the model. The
2025-03-04T20:59:01.3978129Z         second does it during the parameter update phase by averaging all buffers.
2025-03-04T20:59:01.3978761Z         Empirical evidence has shown that updating the statistics in normalization
2025-03-04T20:59:01.3979520Z         layers increases accuracy, but you may wish to empirically test which
2025-03-04T20:59:01.3980053Z         approach yields the best results in your problem.
2025-03-04T20:59:01.3980348Z 
2025-03-04T20:59:01.3980450Z     .. note::
2025-03-04T20:59:01.3980864Z         :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2025-03-04T20:59:01.3981260Z 
2025-03-04T20:59:01.3981355Z     .. note::
2025-03-04T20:59:01.3981769Z         When :meth:`update_parameters` is called for the first time (i.e.
2025-03-04T20:59:01.3982302Z         :attr:`n_averaged` is `0`) the parameters of `model` are copied
2025-03-04T20:59:01.3982835Z         to the parameters of :class:`AveragedModel`. For every subsequent
2025-03-04T20:59:01.3983371Z         call of :meth:`update_parameters` the function `avg_fn` is used
2025-03-04T20:59:01.3983812Z         to update the parameters.
2025-03-04T20:59:01.3984021Z 
2025-03-04T20:59:01.3984271Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-04T20:59:01.3984775Z         https://arxiv.org/abs/1803.05407
2025-03-04T20:59:01.3985266Z     .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2025-03-04T20:59:01.3985812Z         Average:
2025-03-04T20:59:01.3986099Z         https://arxiv.org/abs/1806.05594
2025-03-04T20:59:01.3986600Z     .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2025-03-04T20:59:01.3987061Z         https://arxiv.org/abs/1904.11943
2025-03-04T20:59:01.3987534Z     .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2025-03-04T20:59:01.3988007Z         Generalizes Well:
2025-03-04T20:59:01.3988309Z         https://arxiv.org/abs/2001.02312
2025-03-04T20:59:01.3988657Z     .. _Polyak averaging:
2025-03-04T20:59:01.3989026Z         https://paperswithcode.com/method/polyak-averaging
2025-03-04T20:59:01.3989410Z     
2025-03-04T20:59:01.3989799Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.3990186Z 
2025-03-04T20:59:01.3990710Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=369.
2025-03-04T20:59:01.3991590Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.3992206Z Anneals the learning rate in each parameter group to a fixed value.
2025-03-04T20:59:01.3992557Z 
2025-03-04T20:59:01.3992798Z     This learning rate scheduler is meant to be used with Stochastic Weight
2025-03-04T20:59:01.3993386Z     Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2025-03-04T20:59:01.3993736Z 
2025-03-04T20:59:01.3993829Z     Args:
2025-03-04T20:59:01.3994147Z         optimizer (torch.optim.Optimizer): wrapped optimizer
2025-03-04T20:59:01.3994710Z         swa_lrs (float or list): the learning rate value for all param groups
2025-03-04T20:59:01.3995293Z             together or separately for each group.
2025-03-04T20:59:01.3995767Z         annealing_epochs (int): number of epochs in the annealing phase
2025-03-04T20:59:01.3996212Z             (default: 10)
2025-03-04T20:59:01.3996630Z         annealing_strategy (str): "cos" or "linear"; specifies the annealing
2025-03-04T20:59:01.3997202Z             strategy: "cos" for cosine annealing, "linear" for linear annealing
2025-03-04T20:59:01.3997657Z             (default: "cos")
2025-03-04T20:59:01.3998048Z         last_epoch (int): the index of the last epoch (default: -1)
2025-03-04T20:59:01.3998352Z 
2025-03-04T20:59:01.3998555Z     The :class:`SWALR` scheduler can be used together with other
2025-03-04T20:59:01.4016809Z     schedulers to switch to a constant learning rate late in the training
2025-03-04T20:59:01.4017384Z     as in the example below.
2025-03-04T20:59:01.4017598Z 
2025-03-04T20:59:01.4017714Z     Example:
2025-03-04T20:59:01.4018086Z         >>> # xdoctest: +SKIP("Undefined variables")
2025-03-04T20:59:01.4018473Z         >>> loader, optimizer, model = ...
2025-03-04T20:59:01.4018838Z         >>> lr_lambda = lambda epoch: 0.9
2025-03-04T20:59:01.4019493Z         >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2025-03-04T20:59:01.4019971Z         >>>        lr_lambda=lr_lambda)
2025-03-04T20:59:01.4020381Z         >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2025-03-04T20:59:01.4020879Z         >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2025-03-04T20:59:01.4021302Z         >>> swa_start = 160
2025-03-04T20:59:01.4021604Z         >>> for i in range(300):
2025-03-04T20:59:01.4021932Z         >>>      for input, target in loader:
2025-03-04T20:59:01.4022321Z         >>>          optimizer.zero_grad()
2025-03-04T20:59:01.4022699Z         >>>          loss_fn(model(input), target).backward()
2025-03-04T20:59:01.4023078Z         >>>          optimizer.step()
2025-03-04T20:59:01.4023405Z         >>>      if i > swa_start:
2025-03-04T20:59:01.4023717Z         >>>          swa_scheduler.step()
2025-03-04T20:59:01.4024045Z         >>>      else:
2025-03-04T20:59:01.4024328Z         >>>          scheduler.step()
2025-03-04T20:59:01.4024554Z 
2025-03-04T20:59:01.4024789Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-04T20:59:01.4024977Z         https://arxiv.org/abs/1803.05407
2025-03-04T20:59:01.4025067Z     
2025-03-04T20:59:01.4025381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.4025387Z 
2025-03-04T20:59:01.9138402Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1263.
2025-03-04T20:59:01.9139730Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:01.9140291Z Asserts that ``actual`` and ``expected`` are close.
2025-03-04T20:59:01.9140600Z 
2025-03-04T20:59:01.9140988Z     If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2025-03-04T20:59:01.9141490Z 
2025-03-04T20:59:01.9141615Z     .. math::
2025-03-04T20:59:01.9141771Z 
2025-03-04T20:59:01.9142156Z         \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2025-03-04T20:59:01.9142669Z 
2025-03-04T20:59:01.9143023Z     Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2025-03-04T20:59:01.9143785Z     only considered equal to each other if ``equal_nan`` is ``True``.
2025-03-04T20:59:01.9144118Z 
2025-03-04T20:59:01.9144342Z     In addition, they are only considered close if they have the same
2025-03-04T20:59:01.9144670Z 
2025-03-04T20:59:01.9144886Z     - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2025-03-04T20:59:01.9145538Z     - ``dtype`` (if ``check_dtype`` is ``True``),
2025-03-04T20:59:01.9145956Z     - ``layout`` (if ``check_layout`` is ``True``), and
2025-03-04T20:59:01.9146364Z     - stride (if ``check_stride`` is ``True``).
2025-03-04T20:59:01.9146611Z 
2025-03-04T20:59:01.9146939Z     If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2025-03-04T20:59:01.9147373Z 
2025-03-04T20:59:01.9147755Z     If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2025-03-04T20:59:01.9148632Z     checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2025-03-04T20:59:01.9149379Z     or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2025-03-04T20:59:01.9150140Z     are always checked for equality whereas the values are checked for closeness according to the definition above.
2025-03-04T20:59:01.9150652Z 
2025-03-04T20:59:01.9150959Z     If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2025-03-04T20:59:01.9151735Z     :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2025-03-04T20:59:01.9152390Z     definition above.
2025-03-04T20:59:01.9152558Z 
2025-03-04T20:59:01.9152874Z     ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2025-03-04T20:59:01.9153722Z     :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2025-03-04T20:59:01.9154615Z     have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2025-03-04T20:59:01.9155498Z     or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2025-03-04T20:59:01.9156245Z     their elements are considered close according to the above definition.
2025-03-04T20:59:01.9156598Z 
2025-03-04T20:59:01.9156713Z     .. note::
2025-03-04T20:59:01.9156850Z 
2025-03-04T20:59:01.9157194Z         Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2025-03-04T20:59:01.9157995Z         :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2025-03-04T20:59:01.9158741Z         Python scalars of different types can be checked, but require ``check_dtype=False``.
2025-03-04T20:59:01.9159247Z 
2025-03-04T20:59:01.9159341Z     Args:
2025-03-04T20:59:01.9159595Z         actual (Any): Actual input.
2025-03-04T20:59:01.9160006Z         expected (Any): Expected input.
2025-03-04T20:59:01.9160613Z         allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2025-03-04T20:59:01.9161270Z             are allowed. Otherwise type equality is required.
2025-03-04T20:59:01.9161936Z         rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2025-03-04T20:59:01.9162708Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-04T20:59:01.9163479Z         atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2025-03-04T20:59:01.9164247Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-04T20:59:01.9164901Z         equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2025-03-04T20:59:01.9165578Z         check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2025-03-04T20:59:01.9166256Z             :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2025-03-04T20:59:01.9166883Z             :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2025-03-04T20:59:01.9167643Z         check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2025-03-04T20:59:01.9168487Z             check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2025-03-04T20:59:01.9169139Z             :func:`torch.promote_types`) before being compared.
2025-03-04T20:59:01.9169801Z         check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2025-03-04T20:59:01.9170635Z             check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2025-03-04T20:59:01.9171212Z             compared.
2025-03-04T20:59:01.9171767Z         check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2025-03-04T20:59:01.9172634Z         msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2025-03-04T20:59:01.9173485Z             the comparison. Can also passed as callable in which case it will be called with the generated message and
2025-03-04T20:59:01.9174358Z             should return the new message.
2025-03-04T20:59:01.9174608Z 
2025-03-04T20:59:01.9174706Z     Raises:
2025-03-04T20:59:01.9175089Z         ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2025-03-04T20:59:01.9175699Z         ValueError: If only ``rtol`` or ``atol`` is specified.
2025-03-04T20:59:01.9176333Z         AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2025-03-04T20:59:01.9177173Z         AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2025-03-04T20:59:01.9177854Z             different types.
2025-03-04T20:59:01.9178427Z         AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2025-03-04T20:59:01.9179300Z         AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2025-03-04T20:59:01.9180132Z         AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2025-03-04T20:59:01.9180898Z         AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2025-03-04T20:59:01.9181462Z             :attr:`~torch.Tensor.layout`.
2025-03-04T20:59:01.9181932Z         AssertionError: If only one of corresponding tensors is quantized.
2025-03-04T20:59:01.9182737Z         AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2025-03-04T20:59:01.9183626Z         AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2025-03-04T20:59:01.9184180Z             :attr:`~torch.Tensor.device`.
2025-03-04T20:59:01.9184765Z         AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2025-03-04T20:59:01.9185606Z         AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2025-03-04T20:59:01.9186473Z         AssertionError: If the values of corresponding tensors are not close according to the definition above.
2025-03-04T20:59:01.9186965Z 
2025-03-04T20:59:01.9187352Z     The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2025-03-04T20:59:01.9188007Z     ``dtype``'s, the maximum of both tolerances is used.
2025-03-04T20:59:01.9188277Z 
2025-03-04T20:59:01.9188431Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9188830Z     | ``dtype``                 | ``rtol``   | ``atol`` |
2025-03-04T20:59:01.9189203Z     +===========================+============+==========+
2025-03-04T20:59:01.9189577Z     | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2025-03-04T20:59:01.9189981Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9190383Z     | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2025-03-04T20:59:01.9190824Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9191223Z     | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9191630Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9192026Z     | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2025-03-04T20:59:01.9192419Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9192823Z     | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2025-03-04T20:59:01.9193222Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9193619Z     | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9194018Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9194417Z     | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2025-03-04T20:59:01.9194819Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9195214Z     | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9195612Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9196003Z     | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9196415Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9196846Z     | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9197244Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9197645Z     | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9198039Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9198439Z     | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:01.9198841Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9199229Z     | other                     | ``0.0``    | ``0.0``  |
2025-03-04T20:59:01.9199626Z     +---------------------------+------------+----------+
2025-03-04T20:59:01.9199870Z 
2025-03-04T20:59:01.9199994Z     .. note::
2025-03-04T20:59:01.9200135Z 
2025-03-04T20:59:01.9200547Z         :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2025-03-04T20:59:01.9201436Z         to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2025-03-04T20:59:01.9202200Z         define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2025-03-04T20:59:01.9202628Z 
2025-03-04T20:59:01.9202754Z         >>> import functools
2025-03-04T20:59:01.9203252Z         >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2025-03-04T20:59:01.9203770Z         >>> assert_equal(1e-9, 1e-10)
2025-03-04T20:59:01.9204127Z         Traceback (most recent call last):
2025-03-04T20:59:01.9204464Z         ...
2025-03-04T20:59:01.9204737Z         AssertionError: Scalars are not equal!
2025-03-04T20:59:01.9205085Z         <BLANKLINE>
2025-03-04T20:59:01.9205351Z         Expected 1e-10 but got 1e-09.
2025-03-04T20:59:01.9205712Z         Absolute difference: 9.000000000000001e-10
2025-03-04T20:59:01.9206081Z         Relative difference: 9.0
2025-03-04T20:59:01.9206296Z 
2025-03-04T20:59:01.9206392Z     Examples:
2025-03-04T20:59:01.9206660Z         >>> # tensor to tensor comparison
2025-03-04T20:59:01.9207038Z         >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2025-03-04T20:59:01.9207440Z         >>> actual = torch.acos(torch.cos(expected))
2025-03-04T20:59:01.9207854Z         >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:01.9208132Z 
2025-03-04T20:59:01.9208255Z         >>> # scalar to scalar comparison
2025-03-04T20:59:01.9208596Z         >>> import math
2025-03-04T20:59:01.9208892Z         >>> expected = math.sqrt(2.0)
2025-03-04T20:59:01.9209234Z         >>> actual = 2.0 / math.sqrt(2.0)
2025-03-04T20:59:01.9209618Z         >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:01.9209880Z 
2025-03-04T20:59:01.9210030Z         >>> # numpy array to numpy array comparison
2025-03-04T20:59:01.9210436Z         >>> import numpy as np
2025-03-04T20:59:01.9210773Z         >>> expected = np.array([1e0, 1e-1, 1e-2])
2025-03-04T20:59:01.9211154Z         >>> actual = np.arccos(np.cos(expected))
2025-03-04T20:59:01.9211560Z         >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:01.9211824Z 
2025-03-04T20:59:01.9211963Z         >>> # sequence to sequence comparison
2025-03-04T20:59:01.9212324Z         >>> import numpy as np
2025-03-04T20:59:01.9212789Z         >>> # The types of the sequences do not have to match. They only have to have the same
2025-03-04T20:59:01.9213321Z         >>> # length and their elements have to match.
2025-03-04T20:59:01.9213732Z         >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2025-03-04T20:59:01.9214135Z         >>> actual = tuple(expected)
2025-03-04T20:59:01.9214515Z         >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:01.9214793Z 
2025-03-04T20:59:01.9214922Z         >>> # mapping to mapping comparison
2025-03-04T20:59:01.9215298Z         >>> from collections import OrderedDict
2025-03-04T20:59:01.9215665Z         >>> import numpy as np
2025-03-04T20:59:01.9215985Z         >>> foo = torch.tensor(1.0)
2025-03-04T20:59:01.9216301Z         >>> bar = 2.0
2025-03-04T20:59:01.9216618Z         >>> baz = np.array(3.0)
2025-03-04T20:59:01.9217078Z         >>> # The types and a possible ordering of mappings do not have to match. They only
2025-03-04T20:59:01.9217689Z         >>> # have to have the same set of keys and their elements have to match.
2025-03-04T20:59:01.9218336Z         >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2025-03-04T20:59:01.9218827Z         >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2025-03-04T20:59:01.9219253Z         >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:01.9219516Z 
2025-03-04T20:59:01.9219662Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-04T20:59:01.9220032Z         >>> actual = expected.clone()
2025-03-04T20:59:01.9220442Z         >>> # By default, directly related instances can be compared
2025-03-04T20:59:01.9220970Z         >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2025-03-04T20:59:01.9221528Z         >>> # This check can be made more strict with allow_subclasses=False
2025-03-04T20:59:01.9221984Z         >>> torch.testing.assert_close(
2025-03-04T20:59:01.9222476Z         ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2025-03-04T20:59:01.9222901Z         ... )
2025-03-04T20:59:01.9223158Z         Traceback (most recent call last):
2025-03-04T20:59:01.9223491Z         ...
2025-03-04T20:59:01.9223880Z         TypeError: No comparison pair was able to handle inputs of type
2025-03-04T20:59:01.9224448Z         <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2025-03-04T20:59:01.9225060Z         >>> # If the inputs are not directly related, they are never considered close
2025-03-04T20:59:01.9225603Z         >>> torch.testing.assert_close(actual.numpy(), expected)
2025-03-04T20:59:01.9226035Z         Traceback (most recent call last):
2025-03-04T20:59:01.9226369Z         ...
2025-03-04T20:59:01.9226809Z         TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2025-03-04T20:59:01.9227350Z         and <class 'torch.Tensor'>.
2025-03-04T20:59:01.9227852Z         >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2025-03-04T20:59:01.9228384Z         >>> # their type if check_dtype=False.
2025-03-04T20:59:01.9228814Z         >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2025-03-04T20:59:01.9229106Z 
2025-03-04T20:59:01.9229230Z         >>> # NaN != NaN by default.
2025-03-04T20:59:01.9229582Z         >>> expected = torch.tensor(float("Nan"))
2025-03-04T20:59:01.9229951Z         >>> actual = expected.clone()
2025-03-04T20:59:01.9230331Z         >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:01.9230725Z         Traceback (most recent call last):
2025-03-04T20:59:01.9231098Z         ...
2025-03-04T20:59:01.9231370Z         AssertionError: Scalars are not close!
2025-03-04T20:59:01.9231776Z         <BLANKLINE>
2025-03-04T20:59:01.9232054Z         Expected nan but got nan.
2025-03-04T20:59:01.9232426Z         Absolute difference: nan (up to 1e-05 allowed)
2025-03-04T20:59:01.9232856Z         Relative difference: nan (up to 1.3e-06 allowed)
2025-03-04T20:59:01.9233349Z         >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2025-03-04T20:59:01.9233694Z 
2025-03-04T20:59:01.9233832Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-04T20:59:01.9234222Z         >>> actual = torch.tensor([1.0, 4.0, 5.0])
2025-03-04T20:59:01.9234628Z         >>> # The default error message can be overwritten.
2025-03-04T20:59:01.9235208Z         >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
2025-03-04T20:59:01.9235765Z         Traceback (most recent call last):
2025-03-04T20:59:01.9236105Z         ...
2025-03-04T20:59:01.9236412Z         AssertionError: Argh, the tensors are not close!
2025-03-04T20:59:01.9236929Z         >>> # If msg is a callable, it can be used to augment the generated message with
2025-03-04T20:59:01.9237406Z         >>> # extra information
2025-03-04T20:59:01.9237792Z         >>> torch.testing.assert_close(
2025-03-04T20:59:01.9238243Z         ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2025-03-04T20:59:01.9238680Z         ... )
2025-03-04T20:59:01.9238952Z         Traceback (most recent call last):
2025-03-04T20:59:01.9239288Z         ...
2025-03-04T20:59:01.9239541Z         AssertionError: Header
2025-03-04T20:59:01.9239846Z         <BLANKLINE>
2025-03-04T20:59:01.9240109Z         Tensor-likes are not close!
2025-03-04T20:59:01.9240428Z         <BLANKLINE>
2025-03-04T20:59:01.9240704Z         Mismatched elements: 2 / 3 (66.7%)
2025-03-04T20:59:01.9241174Z         Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2025-03-04T20:59:01.9241770Z         Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2025-03-04T20:59:01.9242229Z         <BLANKLINE>
2025-03-04T20:59:01.9242478Z         Footer
2025-03-04T20:59:01.9242716Z     
2025-03-04T20:59:01.9243103Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:01.9243480Z 
2025-03-04T20:59:03.3012626Z 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=104.
2025-03-04T20:59:03.3013879Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3014540Z Register a container-like type as pytree node.
2025-03-04T20:59:03.3014821Z 
2025-03-04T20:59:03.3014919Z     Args:
2025-03-04T20:59:03.3015260Z         cls (type): A Python type to treat as an internal pytree node.
2025-03-04T20:59:03.3015871Z         flatten_fn (callable): A function to be used during flattening, taking an instance of
2025-03-04T20:59:03.3016548Z             ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2025-03-04T20:59:03.3017241Z             recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2025-03-04T20:59:03.3017870Z             passed to the ``unflatten_fn``.
2025-03-04T20:59:03.3018404Z         unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2025-03-04T20:59:03.3019094Z             returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2025-03-04T20:59:03.3019668Z             The function should return an instance of ``cls``.
2025-03-04T20:59:03.3020230Z         serialized_type_name (str, optional): A keyword argument used to specify the fully
2025-03-04T20:59:03.3020807Z             qualified name used when serializing the tree spec.
2025-03-04T20:59:03.3021425Z         to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2025-03-04T20:59:03.3022234Z             to convert the context of the pytree to a custom json dumpable representation. This is
2025-03-04T20:59:03.3022934Z             used for json serialization, which is being used in :mod:`torch.export` right now.
2025-03-04T20:59:03.3023654Z         from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2025-03-04T20:59:03.3024372Z             how to convert the custom json dumpable representation of the context back to the
2025-03-04T20:59:03.3025041Z             original context. This is used for json deserialization, which is being used in
2025-03-04T20:59:03.3025568Z             :mod:`torch.export` right now.
2025-03-04T20:59:03.3025802Z 
2025-03-04T20:59:03.3025943Z     Example::
2025-03-04T20:59:03.3026084Z 
2025-03-04T20:59:03.3026268Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:03.3026624Z         >>> # Registry a Python type with lambda functions
2025-03-04T20:59:03.3027014Z         >>> register_pytree_node(
2025-03-04T20:59:03.3027322Z         ...     set,
2025-03-04T20:59:03.3027608Z         ...     lambda s: (sorted(s), None, None),
2025-03-04T20:59:03.3027994Z         ...     lambda children, _: set(children),
2025-03-04T20:59:03.3028341Z         ... )
2025-03-04T20:59:03.3028586Z     
2025-03-04T20:59:03.3029042Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.3029433Z 
2025-03-04T20:59:03.3610341Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1200.
2025-03-04T20:59:03.3611418Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3611826Z 
2025-03-04T20:59:03.3612101Z Context passed to policy function during selective checkpointing.
2025-03-04T20:59:03.3612496Z 
2025-03-04T20:59:03.3612734Z This class is used to pass relevant metadata to the policy function during
2025-03-04T20:59:03.3613396Z selective checkpointing. The metadata includes whether the current invocation
2025-03-04T20:59:03.3613980Z of the policy function is during recomputation or not.
2025-03-04T20:59:03.3614275Z 
2025-03-04T20:59:03.3614372Z Example:
2025-03-04T20:59:03.3614634Z     >>> # xdoctest: +SKIP(stub)
2025-03-04T20:59:03.3614935Z     >>>
2025-03-04T20:59:03.3615332Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-04T20:59:03.3615769Z     >>>    print(ctx.is_recompute)
2025-03-04T20:59:03.3616492Z     >>>
2025-03-04T20:59:03.3616997Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-04T20:59:03.3617502Z     >>>
2025-03-04T20:59:03.3617971Z     >>> out = torch.utils.checkpoint.checkpoint(
2025-03-04T20:59:03.3618347Z     >>>     fn, x, y,
2025-03-04T20:59:03.3618633Z     >>>     use_reentrant=False,
2025-03-04T20:59:03.3618963Z     >>>     context_fn=context_fn,
2025-03-04T20:59:03.3619275Z     >>> )
2025-03-04T20:59:03.3619404Z 
2025-03-04T20:59:03.3619684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.3620067Z 
2025-03-04T20:59:03.3620759Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1334.
2025-03-04T20:59:03.3621795Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3622192Z 
2025-03-04T20:59:03.3622455Z Helper to avoid recomputing certain ops during activation checkpointing.
2025-03-04T20:59:03.3622820Z 
2025-03-04T20:59:03.3623056Z Use this with `torch.utils.checkpoint.checkpoint` to control which
2025-03-04T20:59:03.3623572Z operations are recomputed during the backward pass.
2025-03-04T20:59:03.3623856Z 
2025-03-04T20:59:03.3623962Z Args:
2025-03-04T20:59:03.3624220Z     policy_fn_or_list (Callable or List):
2025-03-04T20:59:03.3624639Z       - If a policy function is provided, it should accept a
2025-03-04T20:59:03.3625185Z         :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2025-03-04T20:59:03.3625835Z         kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2025-03-04T20:59:03.3626426Z         indicating whether the execution of the op should be recomputed or not.
2025-03-04T20:59:03.3627011Z       - If a list of operations is provided, it is equivalent to a policy
2025-03-04T20:59:03.3627546Z         returning `CheckpointPolicy.MUST_SAVE` for the specified
2025-03-04T20:59:03.3628091Z         operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2025-03-04T20:59:03.3628543Z         operations.
2025-03-04T20:59:03.3628920Z     allow_cache_entry_mutation (bool, optional): By default, an error is
2025-03-04T20:59:03.3629494Z         raised if any tensors cached by selective activation checkpoint are
2025-03-04T20:59:03.3630061Z         mutated in order to ensure correctness. If set to `True`, this check
2025-03-04T20:59:03.3630504Z         is disabled.
2025-03-04T20:59:03.3630768Z Returns:
2025-03-04T20:59:03.3631024Z     A tuple of two context managers.
2025-03-04T20:59:03.3631255Z 
2025-03-04T20:59:03.3631353Z Example:
2025-03-04T20:59:03.3631605Z     >>> # xdoctest: +REQUIRES(LINUX)
2025-03-04T20:59:03.3631936Z     >>> import functools
2025-03-04T20:59:03.3632208Z     >>>
2025-03-04T20:59:03.3632473Z     >>> x = torch.rand(10, 10, requires_grad=True)
2025-03-04T20:59:03.3632913Z     >>> y = torch.rand(10, 10, requires_grad=True)
2025-03-04T20:59:03.3633256Z     >>>
2025-03-04T20:59:03.3633491Z     >>> ops_to_save = [
2025-03-04T20:59:03.3633790Z     >>>    torch.ops.aten.mm.default,
2025-03-04T20:59:03.3634114Z     >>> ]
2025-03-04T20:59:03.3634330Z     >>>
2025-03-04T20:59:03.3634595Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-04T20:59:03.3634958Z     >>>    if op in ops_to_save:
2025-03-04T20:59:03.3635300Z     >>>        return CheckpointPolicy.MUST_SAVE
2025-03-04T20:59:03.3635651Z     >>>    else:
2025-03-04T20:59:03.3635954Z     >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2025-03-04T20:59:03.3636314Z     >>>
2025-03-04T20:59:03.3636733Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-04T20:59:03.3637233Z     >>>
2025-03-04T20:59:03.3637468Z     >>> # or equivalently
2025-03-04T20:59:03.3637935Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2025-03-04T20:59:03.3638438Z     >>>
2025-03-04T20:59:03.3638671Z     >>> def fn(x, y):
2025-03-04T20:59:03.3639088Z     >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2025-03-04T20:59:03.3639517Z     >>>
2025-03-04T20:59:03.3639798Z     >>> out = torch.utils.checkpoint.checkpoint(
2025-03-04T20:59:03.3640150Z     >>>     fn, x, y,
2025-03-04T20:59:03.3640467Z     >>>     use_reentrant=False,
2025-03-04T20:59:03.3640788Z     >>>     context_fn=context_fn,
2025-03-04T20:59:03.3641098Z     >>> )
2025-03-04T20:59:03.3641239Z 
2025-03-04T20:59:03.3641501Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.3641895Z 
2025-03-04T20:59:03.3861477Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1064.
2025-03-04T20:59:03.3862439Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3862849Z 
2025-03-04T20:59:03.3863002Z Create a :class:`setuptools.Extension` for C++.
2025-03-04T20:59:03.3863280Z 
2025-03-04T20:59:03.3863538Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-04T20:59:03.3864140Z bare minimum (but often sufficient) arguments to build a C++ extension.
2025-03-04T20:59:03.3864501Z 
2025-03-04T20:59:03.3864722Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-04T20:59:03.3865214Z constructor. Full list arguments can be found at
2025-03-04T20:59:03.3865829Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-04T20:59:03.3866289Z 
2025-03-04T20:59:03.3866409Z .. warning::
2025-03-04T20:59:03.3866939Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-04T20:59:03.3867527Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-04T20:59:03.3868218Z     the user's responsibility in their library to not use APIs from
2025-03-04T20:59:03.3869083Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-04T20:59:03.3869833Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-04T20:59:03.3870418Z     example, to give access to custom ops from python, the library should
2025-03-04T20:59:03.3870906Z     register the ops through the dispatcher.
2025-03-04T20:59:03.3871168Z 
2025-03-04T20:59:03.3871406Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-04T20:59:03.3871972Z     as a compile flag when py_limited_api is specified as an option for
2025-03-04T20:59:03.3872525Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-04T20:59:03.3873094Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-04T20:59:03.3873911Z     safety, and sanity in order to encourage best practices. To target a
2025-03-04T20:59:03.3874553Z     different version, set min_supported_cpython to the hexcode of the
2025-03-04T20:59:03.3875294Z     CPython version of choice.
2025-03-04T20:59:03.3875528Z 
2025-03-04T20:59:03.3875656Z Example:
2025-03-04T20:59:03.3875911Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:03.3876283Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:03.3876705Z     >>> from setuptools import setup
2025-03-04T20:59:03.3877174Z     >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2025-03-04T20:59:03.3877633Z     >>> setup(
2025-03-04T20:59:03.3877882Z     ...     name='extension',
2025-03-04T20:59:03.3878195Z     ...     ext_modules=[
2025-03-04T20:59:03.3878489Z     ...         CppExtension(
2025-03-04T20:59:03.3878801Z     ...             name='extension',
2025-03-04T20:59:03.3879158Z     ...             sources=['extension.cpp'],
2025-03-04T20:59:03.3879540Z     ...             extra_compile_args=['-g'],
2025-03-04T20:59:03.3879954Z     ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2025-03-04T20:59:03.3880359Z     ...     ],
2025-03-04T20:59:03.3880609Z     ...     cmdclass={
2025-03-04T20:59:03.3880903Z     ...         'build_ext': BuildExtension
2025-03-04T20:59:03.3881324Z     ...     })
2025-03-04T20:59:03.3881464Z 
2025-03-04T20:59:03.3881740Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.3882174Z 
2025-03-04T20:59:03.3882794Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1134.
2025-03-04T20:59:03.3883743Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3884143Z 
2025-03-04T20:59:03.3884396Z Create a :class:`setuptools.Extension` for CUDA/C++.
2025-03-04T20:59:03.3884837Z 
2025-03-04T20:59:03.3885249Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-04T20:59:03.3886074Z bare minimum (but often sufficient) arguments to build a CUDA/C++
2025-03-04T20:59:03.3886808Z extension. This includes the CUDA include path, library path and runtime
2025-03-04T20:59:03.3887274Z library.
2025-03-04T20:59:03.3887415Z 
2025-03-04T20:59:03.3887633Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-04T20:59:03.3888131Z constructor. Full list arguments can be found at
2025-03-04T20:59:03.3888744Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-04T20:59:03.3889213Z 
2025-03-04T20:59:03.3889322Z .. warning::
2025-03-04T20:59:03.3889708Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-04T20:59:03.3890292Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-04T20:59:03.3890847Z     the user's responsibility in their library to not use APIs from
2025-03-04T20:59:03.3891538Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-04T20:59:03.3892133Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-04T20:59:03.3892706Z     example, to give access to custom ops from python, the library should
2025-03-04T20:59:03.3893191Z     register the ops through the dispatcher.
2025-03-04T20:59:03.3893441Z 
2025-03-04T20:59:03.3893688Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-04T20:59:03.3894257Z     as a compile flag when py_limited_api is specified as an option for
2025-03-04T20:59:03.3894814Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-04T20:59:03.3895694Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-04T20:59:03.3896592Z     safety, and sanity in order to encourage best practices. To target a
2025-03-04T20:59:03.3897594Z     different version, set min_supported_cpython to the hexcode of the
2025-03-04T20:59:03.3898148Z     CPython version of choice.
2025-03-04T20:59:03.3898348Z 
2025-03-04T20:59:03.3898461Z Example:
2025-03-04T20:59:03.3898709Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:03.3899057Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:03.3899512Z     >>> from setuptools import setup
2025-03-04T20:59:03.3899980Z     >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2025-03-04T20:59:03.3900445Z     >>> setup(
2025-03-04T20:59:03.3900710Z     ...     name='cuda_extension',
2025-03-04T20:59:03.3901029Z     ...     ext_modules=[
2025-03-04T20:59:03.3901316Z     ...         CUDAExtension(
2025-03-04T20:59:03.3901642Z     ...                 name='cuda_extension',
2025-03-04T20:59:03.3902055Z     ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-04T20:59:03.3902494Z     ...                 extra_compile_args={'cxx': ['-g'],
2025-03-04T20:59:03.3902878Z     ...                                     'nvcc': ['-O2']},
2025-03-04T20:59:03.3903336Z     ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2025-03-04T20:59:03.3903719Z     ...     ],
2025-03-04T20:59:03.3903967Z     ...     cmdclass={
2025-03-04T20:59:03.3904257Z     ...         'build_ext': BuildExtension
2025-03-04T20:59:03.3904588Z     ...     })
2025-03-04T20:59:03.3904727Z 
2025-03-04T20:59:03.3904848Z Compute capabilities:
2025-03-04T20:59:03.3905060Z 
2025-03-04T20:59:03.3905386Z By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-04T20:59:03.3906153Z building process of the extension, plus PTX. If down the road a new card is installed the
2025-03-04T20:59:03.3906878Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2025-03-04T20:59:03.3907620Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
2025-03-04T20:59:03.3908349Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2025-03-04T20:59:03.3908902Z support (see below for details on PTX).
2025-03-04T20:59:03.3909134Z 
2025-03-04T20:59:03.3909464Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2025-03-04T20:59:03.3910035Z CCs you want the extension to support:
2025-03-04T20:59:03.3910264Z 
2025-03-04T20:59:03.3910474Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2025-03-04T20:59:03.3911026Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2025-03-04T20:59:03.3911409Z 
2025-03-04T20:59:03.3911741Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2025-03-04T20:59:03.3912620Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2025-03-04T20:59:03.3913388Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2025-03-04T20:59:03.3914131Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2025-03-04T20:59:03.3914925Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2025-03-04T20:59:03.3915669Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2025-03-04T20:59:03.3916410Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2025-03-04T20:59:03.3917188Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2025-03-04T20:59:03.3917740Z "8.0 8.6" would be better.
2025-03-04T20:59:03.3917931Z 
2025-03-04T20:59:03.3918239Z Note that while it's possible to include all supported archs, the more archs get included the
2025-03-04T20:59:03.3918969Z slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-04T20:59:03.3919419Z 
2025-03-04T20:59:03.3919758Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2025-03-04T20:59:03.3920453Z To workaround the issue, move python binding logic to pure C++ file.
2025-03-04T20:59:03.3920803Z 
2025-03-04T20:59:03.3920906Z Example use:
2025-03-04T20:59:03.3921166Z     #include <ATen/ATen.h>
2025-03-04T20:59:03.3921525Z     at::Tensor SigmoidAlphaBlendForwardCuda(....)
2025-03-04T20:59:03.3921844Z 
2025-03-04T20:59:03.3921942Z Instead of:
2025-03-04T20:59:03.3922208Z     #include <torch/extension.h>
2025-03-04T20:59:03.3922595Z     torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2025-03-04T20:59:03.3922883Z 
2025-03-04T20:59:03.3923177Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2025-03-04T20:59:03.3924108Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2025-03-04T20:59:03.3924749Z 
2025-03-04T20:59:03.3924872Z Relocatable device code linking:
2025-03-04T20:59:03.3925095Z 
2025-03-04T20:59:03.3925387Z If you want to reference device symbols across compilation units (across object files),
2025-03-04T20:59:03.3926076Z the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2025-03-04T20:59:03.3926844Z An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2025-03-04T20:59:03.3927683Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2025-03-04T20:59:03.3928516Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2025-03-04T20:59:03.3929161Z helps reduce the protentional perf degradation of `-rdc`.
2025-03-04T20:59:03.3929679Z Note that it needs to be used at both steps to be useful.
2025-03-04T20:59:03.3929967Z 
2025-03-04T20:59:03.3930355Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2025-03-04T20:59:03.3931032Z There is also a case where `-dlink` is used without `-rdc`:
2025-03-04T20:59:03.3931600Z when an extension is linked against a static lib containing rdc-compiled objects
2025-03-04T20:59:03.3932206Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2025-03-04T20:59:03.3932540Z 
2025-03-04T20:59:03.3932762Z Note: Ninja is required to build a CUDA Extension with RDC linking.
2025-03-04T20:59:03.3933088Z 
2025-03-04T20:59:03.3933194Z Example:
2025-03-04T20:59:03.3933438Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:03.3933783Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:03.3934162Z     >>> CUDAExtension(
2025-03-04T20:59:03.3934448Z     ...        name='cuda_extension',
2025-03-04T20:59:03.3934841Z     ...        sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-04T20:59:03.3935222Z     ...        dlink=True,
2025-03-04T20:59:03.3935734Z     ...        dlink_libraries=["dlink_lib"],
2025-03-04T20:59:03.3936112Z     ...        extra_compile_args={'cxx': ['-g'],
2025-03-04T20:59:03.3936497Z     ...                            'nvcc': ['-O2', '-rdc=true']})
2025-03-04T20:59:03.3936759Z 
2025-03-04T20:59:03.3937068Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.3937461Z 
2025-03-04T20:59:03.3938080Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1325.
2025-03-04T20:59:03.3939029Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3939429Z 
2025-03-04T20:59:03.3939601Z Creates a :class:`setuptools.Extension` for SYCL/C++.
2025-03-04T20:59:03.3939895Z 
2025-03-04T20:59:03.3940148Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-04T20:59:03.3940733Z bare minimum (but often sufficient) arguments to build a SYCL/C++
2025-03-04T20:59:03.3941161Z extension.
2025-03-04T20:59:03.3941306Z 
2025-03-04T20:59:03.3941521Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-04T20:59:03.3941956Z constructor.
2025-03-04T20:59:03.3942107Z 
2025-03-04T20:59:03.3942206Z .. note::
2025-03-04T20:59:03.3942582Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-04T20:59:03.3943166Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-04T20:59:03.3943756Z     the user's responsibility in their library to not use APIs from
2025-03-04T20:59:03.3944325Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-04T20:59:03.3944918Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-04T20:59:03.3945492Z     example, to give access to custom ops from python, the library should
2025-03-04T20:59:03.3945977Z     register the ops through the dispatcher.
2025-03-04T20:59:03.3946323Z 
2025-03-04T20:59:03.3946485Z Example:
2025-03-04T20:59:03.3946746Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:03.3947092Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:03.3947602Z     >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
2025-03-04T20:59:03.3948059Z     >>> setup(
2025-03-04T20:59:03.3948320Z     ...     name='xpu_extension',
2025-03-04T20:59:03.3948622Z     ...     ext_modules=[
2025-03-04T20:59:03.3948911Z     ...     SyclExtension(
2025-03-04T20:59:03.3949229Z     ...                 name='xpu_extension',
2025-03-04T20:59:03.3949646Z     ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
2025-03-04T20:59:03.3950184Z     ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
2025-03-04T20:59:03.3950595Z     ...     ],
2025-03-04T20:59:03.3950841Z     ...     cmdclass={
2025-03-04T20:59:03.3951167Z     ...         'build_ext': BuildExtension
2025-03-04T20:59:03.3951497Z     ...     })
2025-03-04T20:59:03.3951648Z 
2025-03-04T20:59:03.3951956Z By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-04T20:59:03.3952649Z building process of the extension. If down the road a new card is installed the
2025-03-04T20:59:03.3953302Z extension may need to be recompiled. You can override the default behavior using
2025-03-04T20:59:03.3954008Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
2025-03-04T20:59:03.3954546Z to support:
2025-03-04T20:59:03.3954685Z 
2025-03-04T20:59:03.3954912Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
2025-03-04T20:59:03.3955238Z 
2025-03-04T20:59:03.3955557Z Note that while it's possible to include all supported archs, the more archs get included the
2025-03-04T20:59:03.3956290Z slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-04T20:59:03.3956711Z 
2025-03-04T20:59:03.3956873Z Note: Ninja is required to build SyclExtension.
2025-03-04T20:59:03.3957133Z 
2025-03-04T20:59:03.3957410Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.3957787Z 
2025-03-04T20:59:03.3958389Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1494.
2025-03-04T20:59:03.3959292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.3959692Z 
2025-03-04T20:59:03.3959845Z Load a PyTorch C++ extension just-in-time (JIT).
2025-03-04T20:59:03.3960119Z 
2025-03-04T20:59:03.3960338Z To load an extension, a Ninja build file is emitted, which is used to
2025-03-04T20:59:03.3960894Z compile the given sources into a dynamic library. This library is
2025-03-04T20:59:03.3961461Z subsequently loaded into the current Python process as a module and
2025-03-04T20:59:03.3961952Z returned from this function, ready for use.
2025-03-04T20:59:03.3962204Z 
2025-03-04T20:59:03.3962421Z By default, the directory to which the build file is emitted and the
2025-03-04T20:59:03.3963004Z resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2025-03-04T20:59:03.3963584Z ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2025-03-04T20:59:03.3964146Z the name of the extension. This location can be overridden in two ways.
2025-03-04T20:59:03.3964711Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2025-03-04T20:59:03.3965282Z replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2025-03-04T20:59:03.3965898Z into subfolders of this directory. Second, if the ``build_directory``
2025-03-04T20:59:03.3966492Z argument to this function is supplied, it overrides the entire path, i.e.
2025-03-04T20:59:03.3967035Z the library will be compiled into that folder directly.
2025-03-04T20:59:03.3967332Z 
2025-03-04T20:59:03.3967555Z To compile the sources, the default system compiler (``c++``) is used,
2025-03-04T20:59:03.3968151Z which can be overridden by setting the ``CXX`` environment variable. To pass
2025-03-04T20:59:03.3968764Z additional arguments to the compilation process, ``extra_cflags`` or
2025-03-04T20:59:03.3969358Z ``extra_ldflags`` can be provided. For example, to compile your extension
2025-03-04T20:59:03.3969943Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2025-03-04T20:59:03.3970459Z ``extra_cflags`` to pass further include directories.
2025-03-04T20:59:03.3970734Z 
2025-03-04T20:59:03.3971031Z CUDA support with mixed compilation is provided. Simply pass CUDA source
2025-03-04T20:59:03.3971599Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2025-03-04T20:59:03.3972193Z detected and compiled with nvcc rather than the C++ compiler. This includes
2025-03-04T20:59:03.3972791Z passing the CUDA lib64 directory as a library directory, and linking
2025-03-04T20:59:03.3973347Z ``cudart``. You can pass additional flags to nvcc via
2025-03-04T20:59:03.3973989Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2025-03-04T20:59:03.3974572Z heuristics for finding the CUDA install directory are used, which usually
2025-03-04T20:59:03.3975161Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2025-03-04T20:59:03.3975612Z safest option.
2025-03-04T20:59:03.3975761Z 
2025-03-04T20:59:03.3976021Z SYCL support with mixed compilation is provided. Simply pass SYCL source
2025-03-04T20:59:03.3976584Z files (``.sycl``) along with other sources. Such files will be detected
2025-03-04T20:59:03.3977144Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
2025-03-04T20:59:03.3977708Z than the C++ compiler. You can pass additional flags to SYCL compiler
2025-03-04T20:59:03.3978326Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
2025-03-04T20:59:03.3978870Z SYCL compiler is expected to be found via system PATH environment
2025-03-04T20:59:03.3979303Z variable.
2025-03-04T20:59:03.3979446Z 
2025-03-04T20:59:03.3979538Z Args:
2025-03-04T20:59:03.3979892Z     name: The name of the extension to build. This MUST be the same as the
2025-03-04T20:59:03.3980357Z         name of the pybind11 module!
2025-03-04T20:59:03.3980797Z     sources: A list of relative or absolute paths to C++ source files.
2025-03-04T20:59:03.3981434Z     extra_cflags: optional list of compiler flags to forward to the build.
2025-03-04T20:59:03.3982024Z     extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2025-03-04T20:59:03.3982495Z         when building CUDA sources.
2025-03-04T20:59:03.3982949Z     extra_sycl_cflags: optional list of compiler flags to forward to SYCL
2025-03-04T20:59:03.3983435Z         compiler when building SYCL sources.
2025-03-04T20:59:03.3983912Z     extra_ldflags: optional list of linker flags to forward to the build.
2025-03-04T20:59:03.3984492Z     extra_include_paths: optional list of include directories to forward
2025-03-04T20:59:03.3984946Z         to the build.
2025-03-04T20:59:03.3985307Z     build_directory: optional path to use as build workspace.
2025-03-04T20:59:03.3985801Z     verbose: If ``True``, turns on verbose logging of load steps.
2025-03-04T20:59:03.3986339Z     with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-04T20:59:03.3986865Z         the build. If set to ``None`` (default), this value is
2025-03-04T20:59:03.3987368Z         automatically determined based on the existence of ``.cu`` or
2025-03-04T20:59:03.3987872Z         ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2025-03-04T20:59:03.3988342Z         and libraries to be included.
2025-03-04T20:59:03.3988798Z     with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-04T20:59:03.3989325Z         the build. If set to ``None`` (default), this value is
2025-03-04T20:59:03.3989838Z         automatically determined based on the existence of ``.sycl`` in
2025-03-04T20:59:03.3990361Z         ``sources``. Set it to `True`` to force SYCL headers and
2025-03-04T20:59:03.3990772Z         libraries to be included.
2025-03-04T20:59:03.3991198Z     is_python_module: If ``True`` (default), imports the produced shared
2025-03-04T20:59:03.3991731Z         library as a Python module. If ``False``, behavior depends on
2025-03-04T20:59:03.3992156Z         ``is_standalone``.
2025-03-04T20:59:03.3992563Z     is_standalone: If ``False`` (default) loads the constructed extension
2025-03-04T20:59:03.3993107Z         into the process as a plain dynamic library. If ``True``, build a
2025-03-04T20:59:03.3993548Z         standalone executable.
2025-03-04T20:59:03.3993748Z 
2025-03-04T20:59:03.3993854Z Returns:
2025-03-04T20:59:03.3994108Z     If ``is_python_module`` is ``True``:
2025-03-04T20:59:03.3994578Z         Returns the loaded PyTorch extension as a Python module.
2025-03-04T20:59:03.3994880Z 
2025-03-04T20:59:03.3995108Z     If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2025-03-04T20:59:03.3995707Z         Returns nothing. (The shared library is loaded into the process as
2025-03-04T20:59:03.3996157Z         a side effect.)
2025-03-04T20:59:03.3996322Z 
2025-03-04T20:59:03.3996452Z     If ``is_standalone`` is ``True``.
2025-03-04T20:59:03.3996886Z         Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2025-03-04T20:59:03.3997407Z         added to the PATH environment variable as a side effect.)
2025-03-04T20:59:03.3997704Z 
2025-03-04T20:59:03.3997813Z Example:
2025-03-04T20:59:03.3998054Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:03.3998372Z     >>> from torch.utils.cpp_extension import load
2025-03-04T20:59:03.3998738Z     >>> module = load(
2025-03-04T20:59:03.3999017Z     ...     name='extension',
2025-03-04T20:59:03.3999386Z     ...     sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-04T20:59:03.3999787Z     ...     extra_cflags=['-O2'],
2025-03-04T20:59:03.4000102Z     ...     verbose=True)
2025-03-04T20:59:03.4000287Z 
2025-03-04T20:59:03.4000552Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.4000943Z 
2025-03-04T20:59:03.4001483Z 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=1803.
2025-03-04T20:59:03.4002410Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.4002810Z 
2025-03-04T20:59:03.4003057Z Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2025-03-04T20:59:03.4003404Z 
2025-03-04T20:59:03.4003647Z This function behaves exactly like :func:`load`, but takes its sources as
2025-03-04T20:59:03.4004249Z strings rather than filenames. These strings are stored to files in the
2025-03-04T20:59:03.4004837Z build directory, after which the behavior of :func:`load_inline` is
2025-03-04T20:59:03.4005289Z identical to :func:`load`.
2025-03-04T20:59:03.4005490Z 
2025-03-04T20:59:03.4005589Z See `the
2025-03-04T20:59:03.4006061Z tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2025-03-04T20:59:03.4006653Z for good examples of using this function.
2025-03-04T20:59:03.4006892Z 
2025-03-04T20:59:03.4007158Z Sources may omit two required parts of a typical non-inline C++ extension:
2025-03-04T20:59:03.4007776Z the necessary header includes, as well as the (pybind11) binding code. More
2025-03-04T20:59:03.4008410Z precisely, strings passed to ``cpp_sources`` are first concatenated into a
2025-03-04T20:59:03.4008984Z single ``.cpp`` file. This file is then prepended with ``#include
2025-03-04T20:59:03.4009415Z <torch/extension.h>``.
2025-03-04T20:59:03.4009618Z 
2025-03-04T20:59:03.4009865Z Furthermore, if the ``functions`` argument is supplied, bindings will be
2025-03-04T20:59:03.4010476Z automatically generated for each function specified. ``functions`` can
2025-03-04T20:59:03.4011081Z either be a list of function names, or a dictionary mapping from function
2025-03-04T20:59:03.4011674Z names to docstrings. If a list is given, the name of each function is used
2025-03-04T20:59:03.4012217Z as its docstring.
2025-03-04T20:59:03.4012378Z 
2025-03-04T20:59:03.4012619Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2025-03-04T20:59:03.4013158Z file and  prepended with ``torch/types.h``, ``cuda.h`` and
2025-03-04T20:59:03.4013719Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2025-03-04T20:59:03.4014301Z separately, but ultimately linked into a single library. Note that no
2025-03-04T20:59:03.4014887Z bindings are generated for functions in ``cuda_sources`` per se. To bind
2025-03-04T20:59:03.4015477Z to a CUDA kernel, you must create a C++ function that calls it, and either
2025-03-04T20:59:03.4016053Z declare or define this C++ function in one of the ``cpp_sources`` (and
2025-03-04T20:59:03.4016622Z include its name in ``functions``).
2025-03-04T20:59:03.4016858Z 
2025-03-04T20:59:03.4017088Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
2025-03-04T20:59:03.4017695Z file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
2025-03-04T20:59:03.4018319Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
2025-03-04T20:59:03.4018871Z linked into a single library. Note that no bindings are generated for
2025-03-04T20:59:03.4019440Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
2025-03-04T20:59:03.4020008Z create a C++ function that calls it, and either declare or define this
2025-03-04T20:59:03.4020544Z C++ function in one of the ``cpp_sources`` (and include its name
2025-03-04T20:59:03.4020968Z in ``functions``).
2025-03-04T20:59:03.4021139Z 
2025-03-04T20:59:03.4021330Z See :func:`load` for a description of arguments omitted below.
2025-03-04T20:59:03.4021645Z 
2025-03-04T20:59:03.4021736Z Args:
2025-03-04T20:59:03.4022092Z     cpp_sources: A string, or list of strings, containing C++ source code.
2025-03-04T20:59:03.4022673Z     cuda_sources: A string, or list of strings, containing CUDA source code.
2025-03-04T20:59:03.4023265Z     sycl_sources: A string, or list of strings, containing SYCL source code.
2025-03-04T20:59:03.4023836Z     functions: A list of function names for which to generate function
2025-03-04T20:59:03.4024400Z         bindings. If a dictionary is given, it should map function names to
2025-03-04T20:59:03.4024943Z         docstrings (which are otherwise just the function names).
2025-03-04T20:59:03.4025527Z     with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-04T20:59:03.4026056Z         the build. If set to ``None`` (default), this value is
2025-03-04T20:59:03.4026561Z         automatically determined based on whether ``cuda_sources`` is
2025-03-04T20:59:03.4027060Z         provided. Set it to ``True`` to force CUDA headers
2025-03-04T20:59:03.4027462Z         and libraries to be included.
2025-03-04T20:59:03.4027948Z     with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-04T20:59:03.4028470Z         the build. If set to ``None`` (default), this value is
2025-03-04T20:59:03.4028970Z         automatically determined based on whether ``sycl_sources`` is
2025-03-04T20:59:03.4029456Z         provided. Set it to ``True`` to force SYCL headers
2025-03-04T20:59:03.4029857Z         and libraries to be included.
2025-03-04T20:59:03.4030305Z     with_pytorch_error_handling: Determines whether pytorch error and
2025-03-04T20:59:03.4030856Z         warning macros are handled by pytorch instead of pybind. To do
2025-03-04T20:59:03.4031414Z         this, each function ``foo`` is called via an intermediary ``_safe_foo``
2025-03-04T20:59:03.4031979Z         function. This redirection might cause issues in obscure cases
2025-03-04T20:59:03.4032534Z         of cpp. This flag should be set to ``False`` when this redirect
2025-03-04T20:59:03.4032953Z         causes issues.
2025-03-04T20:59:03.4033132Z 
2025-03-04T20:59:03.4033228Z Example:
2025-03-04T20:59:03.4033522Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:03.4033970Z     >>> from torch.utils.cpp_extension import load_inline
2025-03-04T20:59:03.4034356Z     >>> source = """
2025-03-04T20:59:03.4034682Z     at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2025-03-04T20:59:03.4035060Z       return x.sin() + y.sin();
2025-03-04T20:59:03.4035354Z     }
2025-03-04T20:59:03.4035575Z     """
2025-03-04T20:59:03.4035855Z     >>> module = load_inline(name='inline_extension',
2025-03-04T20:59:03.4036248Z     ...                      cpp_sources=[source],
2025-03-04T20:59:03.4036620Z     ...                      functions=['sin_add'])
2025-03-04T20:59:03.4036858Z 
2025-03-04T20:59:03.4036968Z .. note::
2025-03-04T20:59:03.4037342Z     Since load_inline will just-in-time compile the source code, please ensure
2025-03-04T20:59:03.4037983Z     that you have the right toolchains installed in the runtime. For example,
2025-03-04T20:59:03.4038574Z     when loading C++, make sure a C++ compiler is available. If you're loading
2025-03-04T20:59:03.4039206Z     a CUDA extension, you will need to additionally install the corresponding CUDA
2025-03-04T20:59:03.4039844Z     toolkit (nvcc and any other dependencies your code has). Compiling toolchains
2025-03-04T20:59:03.4040473Z     are not included when you install torch and must be additionally installed.
2025-03-04T20:59:03.4040847Z 
2025-03-04T20:59:03.4041112Z     During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
2025-03-04T20:59:03.4041728Z     the extension. This may use up too many resources on some systems. One
2025-03-04T20:59:03.4042310Z     can control the number of workers by setting the `MAX_JOBS` environment
2025-03-04T20:59:03.4042790Z     variable to a non-negative number.
2025-03-04T20:59:03.4043027Z 
2025-03-04T20:59:03.4043287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.4043675Z 
2025-03-04T20:59:03.4129719Z 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.
2025-03-04T20:59:03.4130742Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.4131145Z 
2025-03-04T20:59:03.4131458Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2025-03-04T20:59:03.4131887Z 
2025-03-04T20:59:03.4132285Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2025-03-04T20:59:03.4132983Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2025-03-04T20:59:03.4133612Z server like load. It can emulate multiple calling threads to a single module
2025-03-04T20:59:03.4134232Z provided. In the future we plan to enhance this component to support inter and
2025-03-04T20:59:03.4134866Z intra-op parallelism as well as multiple models running in a single process.
2025-03-04T20:59:03.4135249Z 
2025-03-04T20:59:03.4135521Z Please note that even though nn.Module is supported, it might incur an overhead
2025-03-04T20:59:03.4136137Z from the need to hold GIL every time we execute Python code or pass around
2025-03-04T20:59:03.4136740Z inputs as Python objects. As soon as you have a ScriptModule version of your
2025-03-04T20:59:03.4137357Z model for inference deployment it is better to switch to using it in this
2025-03-04T20:59:03.4137928Z benchmark.
2025-03-04T20:59:03.4138081Z 
2025-03-04T20:59:03.4138182Z Example::
2025-03-04T20:59:03.4138330Z 
2025-03-04T20:59:03.4138459Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:03.4138860Z     >>> from torch.utils import ThroughputBenchmark
2025-03-04T20:59:03.4139275Z     >>> bench = ThroughputBenchmark(my_module)
2025-03-04T20:59:03.4139763Z     >>> # Pre-populate benchmark's data set with the inputs
2025-03-04T20:59:03.4140168Z     >>> for input in inputs:
2025-03-04T20:59:03.4140598Z     ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2025-03-04T20:59:03.4141095Z     ...     bench.add_input(input[0], x2=input[1])
2025-03-04T20:59:03.4141566Z     >>> # Inputs supplied above are randomly used during the execution
2025-03-04T20:59:03.4142015Z     >>> stats = bench.benchmark(
2025-03-04T20:59:03.4142343Z     ...     num_calling_threads=4,
2025-03-04T20:59:03.4142676Z     ...     num_warmup_iters = 100,
2025-03-04T20:59:03.4143006Z     ...     num_iters = 1000,
2025-03-04T20:59:03.4143298Z     ... )
2025-03-04T20:59:03.4143614Z     >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2025-03-04T20:59:03.4144107Z     >>> print("Number of iterations: {}".format(stats.num_iters))
2025-03-04T20:59:03.4144417Z 
2025-03-04T20:59:03.4144681Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.4145075Z 
2025-03-04T20:59:03.5316552Z 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.
2025-03-04T20:59:03.5317815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:03.5318538Z Sampler that restricts data loading to a subset of the dataset.
2025-03-04T20:59:03.5318867Z 
2025-03-04T20:59:03.5319028Z     It is especially useful in conjunction with
2025-03-04T20:59:03.5319547Z     :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2025-03-04T20:59:03.5320197Z     process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2025-03-04T20:59:03.5320834Z     :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2025-03-04T20:59:03.5321326Z     original dataset that is exclusive to it.
2025-03-04T20:59:03.5321587Z 
2025-03-04T20:59:03.5321711Z     .. note::
2025-03-04T20:59:03.5322109Z         Dataset is assumed to be of constant size and that any instance of it always
2025-03-04T20:59:03.5322650Z         returns the same elements in the same order.
2025-03-04T20:59:03.5322921Z 
2025-03-04T20:59:03.5323015Z     Args:
2025-03-04T20:59:03.5323283Z         dataset: Dataset used for sampling.
2025-03-04T20:59:03.5323753Z         num_replicas (int, optional): Number of processes participating in
2025-03-04T20:59:03.5324364Z             distributed training. By default, :attr:`world_size` is retrieved from the
2025-03-04T20:59:03.5324871Z             current distributed group.
2025-03-04T20:59:03.5325358Z         rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2025-03-04T20:59:03.5325997Z             By default, :attr:`rank` is retrieved from the current distributed
2025-03-04T20:59:03.5326432Z             group.
2025-03-04T20:59:03.5326827Z         shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2025-03-04T20:59:03.5327286Z             indices.
2025-03-04T20:59:03.5343495Z         seed (int, optional): random seed used to shuffle the sampler if
2025-03-04T20:59:03.5344314Z             :attr:`shuffle=True`. This number should be identical across all
2025-03-04T20:59:03.5344930Z             processes in the distributed group. Default: ``0``.
2025-03-04T20:59:03.5345467Z         drop_last (bool, optional): if ``True``, then the sampler will drop the
2025-03-04T20:59:03.5346034Z             tail of the data to make it evenly divisible across the number of
2025-03-04T20:59:03.5346568Z             replicas. If ``False``, the sampler will add extra indices to make
2025-03-04T20:59:03.5347121Z             the data evenly divisible across the replicas. Default: ``False``.
2025-03-04T20:59:03.5347478Z 
2025-03-04T20:59:03.5347592Z     .. warning::
2025-03-04T20:59:03.5347952Z         In distributed mode, calling the :meth:`set_epoch` method at
2025-03-04T20:59:03.5348536Z         the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2025-03-04T20:59:03.5349345Z         is necessary to make shuffling work properly across multiple epochs. Otherwise,
2025-03-04T20:59:03.5349880Z         the same ordering will be always used.
2025-03-04T20:59:03.5350136Z 
2025-03-04T20:59:03.5350237Z     Example::
2025-03-04T20:59:03.5350394Z 
2025-03-04T20:59:03.5350509Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:03.5350945Z         >>> sampler = DistributedSampler(dataset) if is_distributed else None
2025-03-04T20:59:03.5351485Z         >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2025-03-04T20:59:03.5351913Z         ...                     sampler=sampler)
2025-03-04T20:59:03.5352300Z         >>> for epoch in range(start_epoch, n_epochs):
2025-03-04T20:59:03.5352674Z         ...     if is_distributed:
2025-03-04T20:59:03.5353013Z         ...         sampler.set_epoch(epoch)
2025-03-04T20:59:03.5353359Z         ...     train(loader)
2025-03-04T20:59:03.5353649Z     
2025-03-04T20:59:03.5354040Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:03.5354418Z 
2025-03-04T20:59:03.7563324Z gathering tests
2025-03-04T20:59:03.7574726Z running 709 test(s)
2025-03-04T20:59:03.7618590Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1077 <- wrt source file
2025-03-04T20:59:03.7626165Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0
2025-03-04T20:59:03.7627311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1113 <- wrt source file
2025-03-04T20:59:03.7631967Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0
2025-03-04T20:59:03.7633446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1182 <- wrt source file
2025-03-04T20:59:03.7634964Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0
2025-03-04T20:59:03.7636585Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1231 <- wrt source file
2025-03-04T20:59:03.7638077Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0
2025-03-04T20:59:03.7639626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1268 <- wrt source file
2025-03-04T20:59:03.7640852Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0
2025-03-04T20:59:03.7642298Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1423 <- wrt source file
2025-03-04T20:59:03.7643875Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0
2025-03-04T20:59:03.7645242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2523 <- wrt source file
2025-03-04T20:59:03.7647132Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0
2025-03-04T20:59:03.7648668Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2785 <- wrt source file
2025-03-04T20:59:03.7650051Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0
2025-03-04T20:59:03.7651409Z * 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
2025-03-04T20:59:03.7652790Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0
2025-03-04T20:59:03.7654248Z * 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
2025-03-04T20:59:03.7655644Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0
2025-03-04T20:59:03.7656888Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file
2025-03-04T20:59:03.7658119Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0
2025-03-04T20:59:03.7659253Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file
2025-03-04T20:59:03.7660383Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0
2025-03-04T20:59:03.7661542Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file
2025-03-04T20:59:03.8277259Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0
2025-03-04T20:59:03.8278574Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file
2025-03-04T20:59:03.8279917Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0
2025-03-04T20:59:03.8281179Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 672 <- wrt source file
2025-03-04T20:59:03.8286130Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0
2025-03-04T20:59:03.8287471Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 729 <- wrt source file
2025-03-04T20:59:03.8304130Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0
2025-03-04T20:59:03.8305445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1347 <- wrt source file
2025-03-04T20:59:03.8424060Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0
2025-03-04T20:59:03.8427616Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1392 <- wrt source file
2025-03-04T20:59:03.8432567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0
2025-03-04T20:59:03.8433745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1465 <- wrt source file
2025-03-04T20:59:03.8440018Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0
2025-03-04T20:59:03.8441217Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1495 <- wrt source file
2025-03-04T20:59:03.8445926Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0
2025-03-04T20:59:03.8447151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file
2025-03-04T20:59:03.8466467Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0
2025-03-04T20:59:03.8467850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file
2025-03-04T20:59:03.8473044Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0
2025-03-04T20:59:03.8474485Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file
2025-03-04T20:59:03.8476699Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0
2025-03-04T20:59:03.8477873Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 179 <- wrt source file
2025-03-04T20:59:03.8488594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0
2025-03-04T20:59:03.8489708Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 293 <- wrt source file
2025-03-04T20:59:03.8538743Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0
2025-03-04T20:59:03.8540070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1027 <- wrt source file
2025-03-04T20:59:03.8550493Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0
2025-03-04T20:59:03.8551739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1302 <- wrt source file
2025-03-04T20:59:03.8561401Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0
2025-03-04T20:59:03.8562596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1386 <- wrt source file
2025-03-04T20:59:03.8568641Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0
2025-03-04T20:59:03.8569830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1420 <- wrt source file
2025-03-04T20:59:03.8577466Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0
2025-03-04T20:59:03.8578670Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1471 <- wrt source file
2025-03-04T20:59:03.8590934Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0
2025-03-04T20:59:03.8592101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1512 <- wrt source file
2025-03-04T20:59:03.8606436Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0
2025-03-04T20:59:03.8607608Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1548 <- wrt source file
2025-03-04T20:59:03.8622917Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0
2025-03-04T20:59:03.8624089Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1586 <- wrt source file
2025-03-04T20:59:03.8643140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0
2025-03-04T20:59:03.8644283Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1759 <- wrt source file
2025-03-04T20:59:03.8674764Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0
2025-03-04T20:59:03.8676053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1926 <- wrt source file
2025-03-04T20:59:03.8699867Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0
2025-03-04T20:59:03.8701530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2026 <- wrt source file
2025-03-04T20:59:03.8703108Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0
2025-03-04T20:59:03.8704383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2126 <- wrt source file
2025-03-04T20:59:03.8705660Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0
2025-03-04T20:59:03.8706803Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 468 <- wrt source file
2025-03-04T20:59:03.8708224Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0
2025-03-04T20:59:03.8709456Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 528 <- wrt source file
2025-03-04T20:59:03.8710570Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0
2025-03-04T20:59:03.8711741Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 151 <- wrt source file
2025-03-04T20:59:03.8712984Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0
2025-03-04T20:59:03.8714228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 251 <- wrt source file
2025-03-04T20:59:03.8726546Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0
2025-03-04T20:59:03.8727803Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 306 <- wrt source file
2025-03-04T20:59:03.8730904Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0
2025-03-04T20:59:03.8732026Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 499 <- wrt source file
2025-03-04T20:59:03.8748289Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0
2025-03-04T20:59:03.8749379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 605 <- wrt source file
2025-03-04T20:59:03.8764497Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0
2025-03-04T20:59:03.8766030Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 786 <- wrt source file
2025-03-04T20:59:03.8767245Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0
2025-03-04T20:59:03.8768433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 854 <- wrt source file
2025-03-04T20:59:03.8769668Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0
2025-03-04T20:59:03.8770890Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1190 <- wrt source file
2025-03-04T20:59:03.8872870Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0
2025-03-04T20:59:03.8874445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1279 <- wrt source file
2025-03-04T20:59:03.9032273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0
2025-03-04T20:59:03.9033496Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 112 <- wrt source file
2025-03-04T20:59:03.9038658Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0
2025-03-04T20:59:03.9039936Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file
2025-03-04T20:59:03.9070695Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0
2025-03-04T20:59:03.9071952Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1571 <- wrt source file
2025-03-04T20:59:03.9074147Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0
2025-03-04T20:59:03.9075491Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1706 <- wrt source file
2025-03-04T20:59:03.9077375Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0
2025-03-04T20:59:03.9078680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1954 <- wrt source file
2025-03-04T20:59:03.9106559Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0
2025-03-04T20:59:03.9107826Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1973 <- wrt source file
2025-03-04T20:59:03.9113793Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0
2025-03-04T20:59:03.9115471Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file
2025-03-04T20:59:03.9116897Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0
2025-03-04T20:59:03.9118312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 285 <- wrt source file
2025-03-04T20:59:03.9119588Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0
2025-03-04T20:59:03.9120981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 310 <- wrt source file
2025-03-04T20:59:03.9122599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0
2025-03-04T20:59:03.9124072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 384 <- wrt source file
2025-03-04T20:59:03.9125407Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0
2025-03-04T20:59:03.9126843Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 456 <- wrt source file
2025-03-04T20:59:03.9128122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0
2025-03-04T20:59:03.9129299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 922 <- wrt source file
2025-03-04T20:59:03.9130547Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0
2025-03-04T20:59:03.9131716Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file
2025-03-04T20:59:03.9132935Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0
2025-03-04T20:59:03.9134177Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 303 <- wrt source file
2025-03-04T20:59:03.9135487Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0
2025-03-04T20:59:03.9137059Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 340 <- wrt source file
2025-03-04T20:59:03.9138462Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0
2025-03-04T20:59:03.9139954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1793 <- wrt source file
2025-03-04T20:59:03.9142505Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0
2025-03-04T20:59:03.9143909Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 61 <- wrt source file
2025-03-04T20:59:03.9146762Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0
2025-03-04T20:59:03.9148213Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 114 <- wrt source file
2025-03-04T20:59:03.9149487Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0
2025-03-04T20:59:03.9150756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 168 <- wrt source file
2025-03-04T20:59:03.9612569Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0
2025-03-04T20:59:03.9613881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 354 <- wrt source file
2025-03-04T20:59:03.9615676Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0
2025-03-04T20:59:03.9617036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 386 <- wrt source file
2025-03-04T20:59:03.9618618Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0
2025-03-04T20:59:03.9620033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 407 <- wrt source file
2025-03-04T20:59:03.9621843Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0
2025-03-04T20:59:03.9623814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 425 <- wrt source file
2025-03-04T20:59:03.9625585Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0
2025-03-04T20:59:03.9627890Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 440 <- wrt source file
2025-03-04T20:59:03.9630031Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0
2025-03-04T20:59:03.9631941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 455 <- wrt source file
2025-03-04T20:59:03.9633391Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0
2025-03-04T20:59:03.9634617Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 406 <- wrt source file
2025-03-04T20:59:03.9635796Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0
2025-03-04T20:59:03.9636927Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 488 <- wrt source file
2025-03-04T20:59:03.9638078Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0
2025-03-04T20:59:03.9639437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 586 <- wrt source file
2025-03-04T20:59:03.9640732Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0
2025-03-04T20:59:03.9642036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 200 <- wrt source file
2025-03-04T20:59:03.9643421Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0
2025-03-04T20:59:03.9644739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 262 <- wrt source file
2025-03-04T20:59:03.9646076Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0
2025-03-04T20:59:03.9647330Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file
2025-03-04T20:59:03.9648559Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0
2025-03-04T20:59:03.9649713Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file
2025-03-04T20:59:03.9650900Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0
2025-03-04T20:59:03.9652154Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file
2025-03-04T20:59:03.9661384Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0
2025-03-04T20:59:03.9662688Z * 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
2025-03-04T20:59:03.9683379Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0
2025-03-04T20:59:03.9685642Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file
2025-03-04T20:59:03.9688363Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0
2025-03-04T20:59:03.9690549Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file
2025-03-04T20:59:03.9737585Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0
2025-03-04T20:59:03.9739994Z * 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
2025-03-04T20:59:03.9757796Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0
2025-03-04T20:59:03.9760138Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 479 <- wrt source file
2025-03-04T20:59:03.9774179Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0
2025-03-04T20:59:03.9776605Z * 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
2025-03-04T20:59:03.9784044Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0
2025-03-04T20:59:03.9786667Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file
2025-03-04T20:59:03.9839957Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0
2025-03-04T20:59:03.9842400Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 300 <- wrt source file
2025-03-04T20:59:03.9844900Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0
2025-03-04T20:59:03.9847470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 302 <- wrt source file
2025-03-04T20:59:03.9850210Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0
2025-03-04T20:59:03.9852853Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1233 <- wrt source file
2025-03-04T20:59:03.9855517Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0
2025-03-04T20:59:03.9858153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 886 <- wrt source file
2025-03-04T20:59:04.0116608Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0
2025-03-04T20:59:04.0118889Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0, line 323 <- wrt source file
2025-03-04T20:59:04.0121058Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0
2025-03-04T20:59:04.0123449Z * 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
2025-03-04T20:59:04.0126119Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0
2025-03-04T20:59:04.0128568Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 232 <- wrt source file
2025-03-04T20:59:04.0156277Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0
2025-03-04T20:59:04.0158610Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 474 <- wrt source file
2025-03-04T20:59:04.0219757Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0
2025-03-04T20:59:04.0222097Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file
2025-03-04T20:59:04.0682332Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0
2025-03-04T20:59:04.0684736Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file
2025-03-04T20:59:04.0749335Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0
2025-03-04T20:59:04.0751719Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file
2025-03-04T20:59:04.0767661Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0
2025-03-04T20:59:04.0770237Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file
2025-03-04T20:59:04.0772820Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0
2025-03-04T20:59:04.0775442Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file
2025-03-04T20:59:04.0926111Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0
2025-03-04T20:59:04.0928611Z * 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
2025-03-04T20:59:04.0931237Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0
2025-03-04T20:59:04.0933636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file
2025-03-04T20:59:04.0935976Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0
2025-03-04T20:59:04.0938902Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 115 <- wrt source file
2025-03-04T20:59:04.0942005Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0
2025-03-04T20:59:04.0944838Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 128 <- wrt source file
2025-03-04T20:59:04.0947579Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0
2025-03-04T20:59:04.0950317Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 270 <- wrt source file
2025-03-04T20:59:04.0953159Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0
2025-03-04T20:59:04.0955628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 110 <- wrt source file
2025-03-04T20:59:04.0957958Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0
2025-03-04T20:59:04.0960335Z * 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
2025-03-04T20:59:04.0962941Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0
2025-03-04T20:59:04.0965298Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 96 <- wrt source file
2025-03-04T20:59:04.0967553Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0
2025-03-04T20:59:04.0970070Z * 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 1338 <- wrt source file
2025-03-04T20:59:04.0972914Z * 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
2025-03-04T20:59:04.0975636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 99 <- wrt source file
2025-03-04T20:59:04.1269336Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0
2025-03-04T20:59:04.1271814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 230 <- wrt source file
2025-03-04T20:59:04.1350812Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0
2025-03-04T20:59:04.1353451Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 299 <- wrt source file
2025-03-04T20:59:04.1356082Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0
2025-03-04T20:59:04.1358646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 405 <- wrt source file
2025-03-04T20:59:04.1426835Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0
2025-03-04T20:59:04.1429425Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 532 <- wrt source file
2025-03-04T20:59:04.1584903Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0
2025-03-04T20:59:04.1587501Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 704 <- wrt source file
2025-03-04T20:59:04.1738225Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0
2025-03-04T20:59:04.1740843Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 790 <- wrt source file
2025-03-04T20:59:04.1743538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0
2025-03-04T20:59:04.1746123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file
2025-03-04T20:59:04.1748748Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0
2025-03-04T20:59:04.1751480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 161 <- wrt source file
2025-03-04T20:59:04.1809134Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0
2025-03-04T20:59:04.1810494Z * 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
2025-03-04T20:59:04.1815806Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0
2025-03-04T20:59:04.1817232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 446 <- wrt source file
2025-03-04T20:59:04.1818541Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0
2025-03-04T20:59:04.1819793Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file
2025-03-04T20:59:04.1882289Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0
2025-03-04T20:59:04.1883738Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 301 <- wrt source file
2025-03-04T20:59:04.1885116Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0
2025-03-04T20:59:04.1886439Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 992 <- wrt source file
2025-03-04T20:59:04.1932138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0
2025-03-04T20:59:04.1933569Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1057 <- wrt source file
2025-03-04T20:59:04.1934949Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0
2025-03-04T20:59:04.1936267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1278 <- wrt source file
2025-03-04T20:59:04.1950546Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0
2025-03-04T20:59:04.1952137Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1344 <- wrt source file
2025-03-04T20:59:04.1953983Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0
2025-03-04T20:59:04.1955416Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1407 <- wrt source file
2025-03-04T20:59:04.1958580Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0
2025-03-04T20:59:04.1959895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1452 <- wrt source file
2025-03-04T20:59:04.1961178Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0
2025-03-04T20:59:04.1962445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1562 <- wrt source file
2025-03-04T20:59:04.1964280Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0
2025-03-04T20:59:04.1965948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 86 <- wrt source file
2025-03-04T20:59:04.1967535Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0
2025-03-04T20:59:04.1968974Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file
2025-03-04T20:59:04.1970574Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0
2025-03-04T20:59:04.1971958Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file
2025-03-04T20:59:04.1973485Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0
2025-03-04T20:59:04.1975413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file
2025-03-04T20:59:04.1977200Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0
2025-03-04T20:59:04.1978882Z * 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
2025-03-04T20:59:04.1980498Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0
2025-03-04T20:59:04.1982093Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file
2025-03-04T20:59:04.1983753Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0
2025-03-04T20:59:04.1985525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file
2025-03-04T20:59:04.1987133Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0
2025-03-04T20:59:04.1988648Z * 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
2025-03-04T20:59:04.1993245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0
2025-03-04T20:59:04.1996215Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 410 <- wrt source file
2025-03-04T20:59:04.2029556Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0
2025-03-04T20:59:04.2032396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file
2025-03-04T20:59:04.2034951Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0
2025-03-04T20:59:04.2037331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file
2025-03-04T20:59:04.2039645Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0
2025-03-04T20:59:04.2042315Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file
2025-03-04T20:59:04.2044683Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0
2025-03-04T20:59:04.2047081Z * 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
2025-03-04T20:59:04.2049710Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0
2025-03-04T20:59:04.2052293Z * 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
2025-03-04T20:59:04.2054941Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0
2025-03-04T20:59:04.2057564Z * 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
2025-03-04T20:59:04.2060408Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0
2025-03-04T20:59:04.2063060Z * 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
2025-03-04T20:59:04.2065755Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0
2025-03-04T20:59:04.2068427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 208 <- wrt source file
2025-03-04T20:59:04.2071119Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0
2025-03-04T20:59:04.2073489Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 294 <- wrt source file
2025-03-04T20:59:04.2076341Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0
2025-03-04T20:59:04.2078371Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 376 <- wrt source file
2025-03-04T20:59:04.2080062Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0
2025-03-04T20:59:04.2081597Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 458 <- wrt source file
2025-03-04T20:59:04.2083161Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0
2025-03-04T20:59:04.2084645Z * 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
2025-03-04T20:59:04.2086204Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0
2025-03-04T20:59:04.2088837Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file
2025-03-04T20:59:04.2091921Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0
2025-03-04T20:59:04.2094920Z * 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
2025-03-04T20:59:04.2097597Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0
2025-03-04T20:59:04.2100093Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file
2025-03-04T20:59:04.2102971Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0
2025-03-04T20:59:04.2106014Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file
2025-03-04T20:59:04.2109108Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0
2025-03-04T20:59:04.2111673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file
2025-03-04T20:59:04.2114481Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0
2025-03-04T20:59:04.2117159Z * 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
2025-03-04T20:59:04.2119860Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0
2025-03-04T20:59:04.2122167Z * 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
2025-03-04T20:59:04.2123633Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0
2025-03-04T20:59:04.2124953Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 634 <- wrt source file
2025-03-04T20:59:04.2126300Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0
2025-03-04T20:59:04.2127957Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 890 <- wrt source file
2025-03-04T20:59:04.2130207Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0
2025-03-04T20:59:04.2131734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1012 <- wrt source file
2025-03-04T20:59:04.2133201Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0
2025-03-04T20:59:04.2135015Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1138 <- wrt source file
2025-03-04T20:59:04.2137178Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0
2025-03-04T20:59:04.2138730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file
2025-03-04T20:59:04.2140246Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0
2025-03-04T20:59:04.2142500Z * 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
2025-03-04T20:59:04.2144224Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0
2025-03-04T20:59:04.2145882Z * 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
2025-03-04T20:59:04.2147509Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0
2025-03-04T20:59:04.2150007Z * 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
2025-03-04T20:59:04.2151984Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0
2025-03-04T20:59:04.2153433Z * 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
2025-03-04T20:59:04.2154810Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0
2025-03-04T20:59:04.2157825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file
2025-03-04T20:59:04.2159811Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0
2025-03-04T20:59:04.2161721Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file
2025-03-04T20:59:04.2249898Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0
2025-03-04T20:59:04.2253327Z * 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
2025-03-04T20:59:04.2256695Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0
2025-03-04T20:59:04.2259703Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file
2025-03-04T20:59:04.2262537Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0
2025-03-04T20:59:04.2265252Z * 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
2025-03-04T20:59:04.2268085Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0
2025-03-04T20:59:04.2270739Z * 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
2025-03-04T20:59:04.2273295Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0
2025-03-04T20:59:04.2276042Z * 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
2025-03-04T20:59:04.2278786Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0
2025-03-04T20:59:04.2281502Z * 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
2025-03-04T20:59:04.2284401Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0
2025-03-04T20:59:04.2287149Z * 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
2025-03-04T20:59:04.2289933Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0
2025-03-04T20:59:04.2292730Z * 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
2025-03-04T20:59:04.2295655Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0
2025-03-04T20:59:04.2298348Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 108 <- wrt source file
2025-03-04T20:59:04.2300921Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0
2025-03-04T20:59:04.2303411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 130 <- wrt source file
2025-03-04T20:59:04.2306023Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0
2025-03-04T20:59:04.2308549Z * 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
2025-03-04T20:59:04.2310978Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0
2025-03-04T20:59:04.2313642Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file
2025-03-04T20:59:04.2316154Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0
2025-03-04T20:59:04.2318644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file
2025-03-04T20:59:04.2321216Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0
2025-03-04T20:59:04.2323776Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 598 <- wrt source file
2025-03-04T20:59:04.2326299Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0
2025-03-04T20:59:04.2328883Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 658 <- wrt source file
2025-03-04T20:59:04.2331632Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0
2025-03-04T20:59:04.2334468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 710 <- wrt source file
2025-03-04T20:59:04.2337463Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0
2025-03-04T20:59:04.2340353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 47 <- wrt source file
2025-03-04T20:59:04.2343004Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0
2025-03-04T20:59:04.2345586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 125 <- wrt source file
2025-03-04T20:59:04.2348246Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0
2025-03-04T20:59:04.2350832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 222 <- wrt source file
2025-03-04T20:59:04.2353416Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0
2025-03-04T20:59:04.2355915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file
2025-03-04T20:59:04.2358475Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0
2025-03-04T20:59:04.2360986Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 517 <- wrt source file
2025-03-04T20:59:04.2363532Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0
2025-03-04T20:59:04.2366060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 539 <- wrt source file
2025-03-04T20:59:04.2368647Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0
2025-03-04T20:59:04.2371174Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 553 <- wrt source file
2025-03-04T20:59:04.2373860Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0
2025-03-04T20:59:04.2376382Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 575 <- wrt source file
2025-03-04T20:59:04.2378956Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0
2025-03-04T20:59:04.2381510Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 702 <- wrt source file
2025-03-04T20:59:04.2384016Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0
2025-03-04T20:59:04.2386733Z * 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
2025-03-04T20:59:04.2389739Z * 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
2025-03-04T20:59:04.2392616Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file
2025-03-04T20:59:04.2395492Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0
2025-03-04T20:59:04.2398381Z * 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
2025-03-04T20:59:04.2401475Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0
2025-03-04T20:59:04.2404409Z * 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
2025-03-04T20:59:04.2407363Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0
2025-03-04T20:59:04.2410216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file
2025-03-04T20:59:04.2413328Z * 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
2025-03-04T20:59:04.2416269Z * 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 430 <- wrt source file
2025-03-04T20:59:04.2419460Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0
2025-03-04T20:59:04.2422186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file
2025-03-04T20:59:04.2424624Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0
2025-03-04T20:59:04.2426913Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file
2025-03-04T20:59:04.2429176Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0
2025-03-04T20:59:04.2431437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file
2025-03-04T20:59:04.2433779Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0
2025-03-04T20:59:04.2436043Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file
2025-03-04T20:59:04.2438339Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0
2025-03-04T20:59:04.2440816Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file
2025-03-04T20:59:04.2443483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0
2025-03-04T20:59:04.2446104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file
2025-03-04T20:59:04.2448756Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0
2025-03-04T20:59:04.2451304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file
2025-03-04T20:59:04.2453886Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0
2025-03-04T20:59:04.2456518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file
2025-03-04T20:59:04.2459340Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0
2025-03-04T20:59:04.2462122Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file
2025-03-04T20:59:04.2464910Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0
2025-03-04T20:59:04.2467379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file
2025-03-04T20:59:04.2469634Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0
2025-03-04T20:59:04.2471828Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file
2025-03-04T20:59:04.2474191Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0
2025-03-04T20:59:04.2476367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file
2025-03-04T20:59:04.2478738Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0
2025-03-04T20:59:04.2480970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file
2025-03-04T20:59:04.2483274Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0
2025-03-04T20:59:04.2485519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 884 <- wrt source file
2025-03-04T20:59:04.2487811Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0
2025-03-04T20:59:04.2490017Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1000 <- wrt source file
2025-03-04T20:59:04.2492229Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0
2025-03-04T20:59:04.2494412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1099 <- wrt source file
2025-03-04T20:59:04.2496629Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0
2025-03-04T20:59:04.2498897Z * 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
2025-03-04T20:59:04.2501113Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0
2025-03-04T20:59:04.2503342Z * 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
2025-03-04T20:59:04.2505638Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0
2025-03-04T20:59:04.2507946Z * 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
2025-03-04T20:59:04.2510338Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0
2025-03-04T20:59:04.2512873Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file
2025-03-04T20:59:04.2515243Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0
2025-03-04T20:59:04.2517558Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file
2025-03-04T20:59:04.2519855Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0
2025-03-04T20:59:04.2522186Z * 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
2025-03-04T20:59:04.2524628Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0
2025-03-04T20:59:04.2526983Z * 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
2025-03-04T20:59:04.2529420Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0
2025-03-04T20:59:04.2531790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 271 <- wrt source file
2025-03-04T20:59:04.2534219Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0
2025-03-04T20:59:04.2536511Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 336 <- wrt source file
2025-03-04T20:59:04.2538811Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0
2025-03-04T20:59:04.2541167Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 393 <- wrt source file
2025-03-04T20:59:04.2543724Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0
2025-03-04T20:59:04.2546192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 470 <- wrt source file
2025-03-04T20:59:04.2548690Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0
2025-03-04T20:59:04.2551211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 736 <- wrt source file
2025-03-04T20:59:04.2553824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0
2025-03-04T20:59:04.2556259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 178 <- wrt source file
2025-03-04T20:59:04.2558498Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0
2025-03-04T20:59:04.2560779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0, line 715 <- wrt source file
2025-03-04T20:59:04.2563173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0
2025-03-04T20:59:04.2565444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 849 <- wrt source file
2025-03-04T20:59:04.2567674Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0
2025-03-04T20:59:04.2569885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 922 <- wrt source file
2025-03-04T20:59:04.2572143Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0
2025-03-04T20:59:04.2574470Z * 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
2025-03-04T20:59:04.2576806Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0
2025-03-04T20:59:04.2579090Z * 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
2025-03-04T20:59:04.2581376Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0
2025-03-04T20:59:04.2583495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file
2025-03-04T20:59:04.2585538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0
2025-03-04T20:59:04.2587673Z * 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
2025-03-04T20:59:04.2589944Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0
2025-03-04T20:59:04.2592306Z * 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
2025-03-04T20:59:04.2594584Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1
2025-03-04T20:59:04.2596824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file
2025-03-04T20:59:04.2599091Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2
2025-03-04T20:59:04.2601448Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file
2025-03-04T20:59:04.2603974Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0
2025-03-04T20:59:04.2606280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file
2025-03-04T20:59:04.2608418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0
2025-03-04T20:59:04.2610734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 415 <- wrt source file
2025-03-04T20:59:04.2613437Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0
2025-03-04T20:59:04.2615961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 931 <- wrt source file
2025-03-04T20:59:04.2618742Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0
2025-03-04T20:59:04.2621316Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1013 <- wrt source file
2025-03-04T20:59:04.2623866Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0
2025-03-04T20:59:04.2626467Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2537 <- wrt source file
2025-03-04T20:59:04.2629186Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0
2025-03-04T20:59:04.2631816Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3024 <- wrt source file
2025-03-04T20:59:04.2634569Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0
2025-03-04T20:59:04.2637184Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3245 <- wrt source file
2025-03-04T20:59:04.2639837Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0
2025-03-04T20:59:04.2642422Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3343 <- wrt source file
2025-03-04T20:59:04.2645075Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0
2025-03-04T20:59:04.2647716Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3453 <- wrt source file
2025-03-04T20:59:04.2650525Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0
2025-03-04T20:59:04.2653238Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3572 <- wrt source file
2025-03-04T20:59:04.2655955Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0
2025-03-04T20:59:04.2658699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3776 <- wrt source file
2025-03-04T20:59:04.2661464Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0
2025-03-04T20:59:04.2664161Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3915 <- wrt source file
2025-03-04T20:59:04.2666898Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0
2025-03-04T20:59:04.2669437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 4016 <- wrt source file
2025-03-04T20:59:04.2671949Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0
2025-03-04T20:59:04.2674512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 4098 <- wrt source file
2025-03-04T20:59:04.2677018Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter:0
2025-03-04T20:59:04.2679586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4230 <- wrt source file
2025-03-04T20:59:04.2682355Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0
2025-03-04T20:59:04.2685021Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4682 <- wrt source file
2025-03-04T20:59:04.2687716Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0
2025-03-04T20:59:04.2690309Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0, line 5260 <- wrt source file
2025-03-04T20:59:04.2693028Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0
2025-03-04T20:59:04.2695735Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0, line 5362 <- wrt source file
2025-03-04T20:59:04.2698672Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0
2025-03-04T20:59:04.2701114Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/run.py::__doc__:0, line 57 <- wrt source file
2025-03-04T20:59:04.2703260Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/run.py::__doc__:0
2025-03-04T20:59:04.2705535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file
2025-03-04T20:59:04.2707993Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0
2025-03-04T20:59:04.2710725Z * 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
2025-03-04T20:59:04.2713816Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0
2025-03-04T20:59:04.2716458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file
2025-03-04T20:59:04.2719090Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0
2025-03-04T20:59:04.2721628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file
2025-03-04T20:59:04.2724237Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0
2025-03-04T20:59:04.2727031Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 31 <- wrt source file
2025-03-04T20:59:04.2730147Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0
2025-03-04T20:59:04.2733164Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file
2025-03-04T20:59:04.2736124Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0
2025-03-04T20:59:04.2739084Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file
2025-03-04T20:59:04.2742049Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0
2025-03-04T20:59:04.2744824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 55 <- wrt source file
2025-03-04T20:59:04.2747497Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0
2025-03-04T20:59:04.2749987Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0, line 141 <- wrt source file
2025-03-04T20:59:04.2752439Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0
2025-03-04T20:59:04.2755125Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0, line 107 <- wrt source file
2025-03-04T20:59:04.2758227Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0
2025-03-04T20:59:04.2761236Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0, line 23 <- wrt source file
2025-03-04T20:59:04.2764265Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0
2025-03-04T20:59:04.2767259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0, line 49 <- wrt source file
2025-03-04T20:59:04.2770405Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0
2025-03-04T20:59:04.2773457Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0, line 104 <- wrt source file
2025-03-04T20:59:04.2776796Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0
2025-03-04T20:59:04.2779924Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0, line 125 <- wrt source file
2025-03-04T20:59:04.2783069Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0
2025-03-04T20:59:04.2786198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 143 <- wrt source file
2025-03-04T20:59:04.2789397Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0
2025-03-04T20:59:04.2792625Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 182 <- wrt source file
2025-03-04T20:59:04.2795826Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0
2025-03-04T20:59:04.2798967Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 707 <- wrt source file
2025-03-04T20:59:04.2802214Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0
2025-03-04T20:59:04.2805479Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0, line 64 <- wrt source file
2025-03-04T20:59:04.2808937Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0
2025-03-04T20:59:04.2812540Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0, line 145 <- wrt source file
2025-03-04T20:59:04.2816106Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0
2025-03-04T20:59:04.2819252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1375 <- wrt source file
2025-03-04T20:59:04.2822164Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0
2025-03-04T20:59:04.2825054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1434 <- wrt source file
2025-03-04T20:59:04.2828034Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0
2025-03-04T20:59:04.2831011Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0, line 231 <- wrt source file
2025-03-04T20:59:04.2834183Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0
2025-03-04T20:59:04.2837053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0, line 141 <- wrt source file
2025-03-04T20:59:04.2839879Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0
2025-03-04T20:59:04.2842458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::StateDictType:0, line 262 <- wrt source file
2025-03-04T20:59:04.2844894Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::StateDictType:0
2025-03-04T20:59:04.2847669Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 130 <- wrt source file
2025-03-04T20:59:04.2850822Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0
2025-03-04T20:59:04.2854266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0, line 1495 <- wrt source file
2025-03-04T20:59:04.2858007Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0
2025-03-04T20:59:04.2861651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0, line 1615 <- wrt source file
2025-03-04T20:59:04.2865379Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0
2025-03-04T20:59:04.2868961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0, line 1700 <- wrt source file
2025-03-04T20:59:04.2872541Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0
2025-03-04T20:59:04.2875822Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0, line 54 <- wrt source file
2025-03-04T20:59:04.2878767Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0
2025-03-04T20:59:04.2881402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 224 <- wrt source file
2025-03-04T20:59:04.2883827Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0
2025-03-04T20:59:04.2886303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file
2025-03-04T20:59:04.2888886Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0
2025-03-04T20:59:04.2891726Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 43 <- wrt source file
2025-03-04T20:59:04.2895045Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0
2025-03-04T20:59:04.2898249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 114 <- wrt source file
2025-03-04T20:59:04.2901434Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0
2025-03-04T20:59:04.2904338Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 44 <- wrt source file
2025-03-04T20:59:04.2907086Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0
2025-03-04T20:59:04.2909766Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 37 <- wrt source file
2025-03-04T20:59:04.2912744Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0
2025-03-04T20:59:04.2915303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 333 <- wrt source file
2025-03-04T20:59:04.2917907Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0
2025-03-04T20:59:04.2920636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0, line 82 <- wrt source file
2025-03-04T20:59:04.2923671Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0
2025-03-04T20:59:04.2926644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 101 <- wrt source file
2025-03-04T20:59:04.2929650Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0
2025-03-04T20:59:04.2932218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 175 <- wrt source file
2025-03-04T20:59:04.2934542Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0
2025-03-04T20:59:04.2936807Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 346 <- wrt source file
2025-03-04T20:59:04.2939218Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0
2025-03-04T20:59:04.2941445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::remote:0, line 605 <- wrt source file
2025-03-04T20:59:04.2943702Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::remote:0
2025-03-04T20:59:04.2945915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 785 <- wrt source file
2025-03-04T20:59:04.2948191Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_sync:0
2025-03-04T20:59:04.2950423Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 877 <- wrt source file
2025-03-04T20:59:04.2952720Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_async:0
2025-03-04T20:59:04.2955126Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0, line 813 <- wrt source file
2025-03-04T20:59:04.2957588Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0
2025-03-04T20:59:04.2960317Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 251 <- wrt source file
2025-03-04T20:59:04.2963362Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0
2025-03-04T20:59:04.2966192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0, line 235 <- wrt source file
2025-03-04T20:59:04.2968948Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0
2025-03-04T20:59:04.2971678Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 57 <- wrt source file
2025-03-04T20:59:04.2974697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0
2025-03-04T20:59:04.2977656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0, line 88 <- wrt source file
2025-03-04T20:59:04.2980672Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0
2025-03-04T20:59:04.2983483Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0, line 55 <- wrt source file
2025-03-04T20:59:04.2986269Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0
2025-03-04T20:59:04.2989072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 63 <- wrt source file
2025-03-04T20:59:04.2991948Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0
2025-03-04T20:59:04.2994784Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 189 <- wrt source file
2025-03-04T20:59:04.2997785Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0
2025-03-04T20:59:04.3000643Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 333 <- wrt source file
2025-03-04T20:59:04.3003530Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0
2025-03-04T20:59:04.3006067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 28 <- wrt source file
2025-03-04T20:59:04.3008480Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0
2025-03-04T20:59:04.3010707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 19 <- wrt source file
2025-03-04T20:59:04.3013195Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0
2025-03-04T20:59:04.3015535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 29 <- wrt source file
2025-03-04T20:59:04.3018001Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0
2025-03-04T20:59:04.3020411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file
2025-03-04T20:59:04.3022918Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0
2025-03-04T20:59:04.3025235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 22 <- wrt source file
2025-03-04T20:59:04.3027519Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0
2025-03-04T20:59:04.3029690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0, line 16 <- wrt source file
2025-03-04T20:59:04.3031881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0
2025-03-04T20:59:04.3034202Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 164 <- wrt source file
2025-03-04T20:59:04.3036764Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0
2025-03-04T20:59:04.3039317Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 185 <- wrt source file
2025-03-04T20:59:04.3041976Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0
2025-03-04T20:59:04.3044704Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file
2025-03-04T20:59:04.3047586Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0
2025-03-04T20:59:04.3050161Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 40 <- wrt source file
2025-03-04T20:59:04.3052577Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0
2025-03-04T20:59:04.3054983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0, line 18 <- wrt source file
2025-03-04T20:59:04.3057534Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0
2025-03-04T20:59:04.3060134Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 19 <- wrt source file
2025-03-04T20:59:04.3062786Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0
2025-03-04T20:59:04.3065167Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0, line 22 <- wrt source file
2025-03-04T20:59:04.3067387Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0
2025-03-04T20:59:04.3069659Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 34 <- wrt source file
2025-03-04T20:59:04.3072072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0
2025-03-04T20:59:04.3074537Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 22 <- wrt source file
2025-03-04T20:59:04.3076881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0
2025-03-04T20:59:04.3079194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file
2025-03-04T20:59:04.3081643Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0
2025-03-04T20:59:04.3084038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file
2025-03-04T20:59:04.3086498Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0
2025-03-04T20:59:04.3088938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file
2025-03-04T20:59:04.3091441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0
2025-03-04T20:59:04.3093915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 22 <- wrt source file
2025-03-04T20:59:04.3096535Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0
2025-03-04T20:59:04.3099086Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file
2025-03-04T20:59:04.3101587Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0
2025-03-04T20:59:04.3103945Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0, line 18 <- wrt source file
2025-03-04T20:59:04.3106272Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0
2025-03-04T20:59:04.3108628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file
2025-03-04T20:59:04.3111132Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0
2025-03-04T20:59:04.3113723Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 21 <- wrt source file
2025-03-04T20:59:04.3116206Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0
2025-03-04T20:59:04.3118706Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 26 <- wrt source file
2025-03-04T20:59:04.3121350Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0
2025-03-04T20:59:04.3123861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file
2025-03-04T20:59:04.3126366Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0
2025-03-04T20:59:04.3129016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 102 <- wrt source file
2025-03-04T20:59:04.3131920Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0
2025-03-04T20:59:04.3134477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0, line 21 <- wrt source file
2025-03-04T20:59:04.3136756Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0
2025-03-04T20:59:04.3139318Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 32 <- wrt source file
2025-03-04T20:59:04.3142104Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0
2025-03-04T20:59:04.3144569Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file
2025-03-04T20:59:04.3146852Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0
2025-03-04T20:59:04.3149119Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file
2025-03-04T20:59:04.3151434Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0
2025-03-04T20:59:04.3153772Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file
2025-03-04T20:59:04.3156140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0
2025-03-04T20:59:04.3158557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1046 <- wrt source file
2025-03-04T20:59:04.3161104Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0
2025-03-04T20:59:04.3163601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1152 <- wrt source file
2025-03-04T20:59:04.3166169Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0
2025-03-04T20:59:04.3168913Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1226 <- wrt source file
2025-03-04T20:59:04.3171878Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0
2025-03-04T20:59:04.3174600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0, line 19 <- wrt source file
2025-03-04T20:59:04.3176913Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0
2025-03-04T20:59:04.3179231Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0, line 109 <- wrt source file
2025-03-04T20:59:04.3181582Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0
2025-03-04T20:59:04.3183891Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0, line 116 <- wrt source file
2025-03-04T20:59:04.3186246Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0
2025-03-04T20:59:04.3211518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0, line 20 <- wrt source file
2025-03-04T20:59:04.3214462Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0
2025-03-04T20:59:04.3216825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file
2025-03-04T20:59:04.3219192Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0
2025-03-04T20:59:04.3221582Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 611 <- wrt source file
2025-03-04T20:59:04.3224104Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0
2025-03-04T20:59:04.3226342Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0, line 95 <- wrt source file
2025-03-04T20:59:04.3228429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0
2025-03-04T20:59:04.3230651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1826 <- wrt source file
2025-03-04T20:59:04.3233029Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0
2025-03-04T20:59:04.3235396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1900 <- wrt source file
2025-03-04T20:59:04.3237714Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0
2025-03-04T20:59:04.3239955Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0, line 48 <- wrt source file
2025-03-04T20:59:04.3242207Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0
2025-03-04T20:59:04.3244418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0, line 464 <- wrt source file
2025-03-04T20:59:04.3246673Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0
2025-03-04T20:59:04.3248979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 125 <- wrt source file
2025-03-04T20:59:04.3251418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0
2025-03-04T20:59:04.3253684Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file
2025-03-04T20:59:04.3255951Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::TensorType:0
2025-03-04T20:59:04.3258183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 65 <- wrt source file
2025-03-04T20:59:04.3260439Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0
2025-03-04T20:59:04.3262650Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file
2025-03-04T20:59:04.3264910Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0
2025-03-04T20:59:04.3267429Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 96 <- wrt source file
2025-03-04T20:59:04.3270231Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0
2025-03-04T20:59:04.3272934Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 58 <- wrt source file
2025-03-04T20:59:04.3275626Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0
2025-03-04T20:59:04.3278212Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0, line 48 <- wrt source file
2025-03-04T20:59:04.3280961Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0
2025-03-04T20:59:04.3283583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file
2025-03-04T20:59:04.3286212Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unifiable:0
2025-03-04T20:59:04.3288806Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file
2025-03-04T20:59:04.3291475Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::reify_object:0
2025-03-04T20:59:04.3294142Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 93 <- wrt source file
2025-03-04T20:59:04.3296805Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unify_object:0
2025-03-04T20:59:04.3299552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0, line 37 <- wrt source file
2025-03-04T20:59:04.3302369Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0
2025-03-04T20:59:04.3305183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0, line 64 <- wrt source file
2025-03-04T20:59:04.3308105Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0
2025-03-04T20:59:04.3310923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0, line 90 <- wrt source file
2025-03-04T20:59:04.3313990Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0
2025-03-04T20:59:04.3316869Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0, line 106 <- wrt source file
2025-03-04T20:59:04.3319733Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0
2025-03-04T20:59:04.3322553Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 122 <- wrt source file
2025-03-04T20:59:04.3325450Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0
2025-03-04T20:59:04.3328286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 138 <- wrt source file
2025-03-04T20:59:04.3331209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0
2025-03-04T20:59:04.3334171Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 158 <- wrt source file
2025-03-04T20:59:04.3337102Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0
2025-03-04T20:59:04.3340049Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0, line 178 <- wrt source file
2025-03-04T20:59:04.3343008Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0
2025-03-04T20:59:04.3345835Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 204 <- wrt source file
2025-03-04T20:59:04.3348686Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0
2025-03-04T20:59:04.3351470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0, line 221 <- wrt source file
2025-03-04T20:59:04.3354337Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0
2025-03-04T20:59:04.3357155Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file
2025-03-04T20:59:04.3359996Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0
2025-03-04T20:59:04.3362723Z * 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
2025-03-04T20:59:04.3365469Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0
2025-03-04T20:59:04.3368099Z * 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
2025-03-04T20:59:04.3370733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0
2025-03-04T20:59:04.3373334Z * 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
2025-03-04T20:59:04.3376255Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0
2025-03-04T20:59:04.3378889Z * 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
2025-03-04T20:59:04.3381481Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0
2025-03-04T20:59:04.3384082Z * 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
2025-03-04T20:59:04.3386813Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0
2025-03-04T20:59:04.3389646Z * 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
2025-03-04T20:59:04.3392667Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0
2025-03-04T20:59:04.3395884Z * 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
2025-03-04T20:59:04.3399111Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0
2025-03-04T20:59:04.3402371Z * 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
2025-03-04T20:59:04.3405786Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0
2025-03-04T20:59:04.3409099Z * 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
2025-03-04T20:59:04.3412618Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0
2025-03-04T20:59:04.3415927Z * 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
2025-03-04T20:59:04.3419480Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0
2025-03-04T20:59:04.3422777Z * 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
2025-03-04T20:59:04.3426047Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0
2025-03-04T20:59:04.3429183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file
2025-03-04T20:59:04.3432310Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0
2025-03-04T20:59:04.3435337Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 41 <- wrt source file
2025-03-04T20:59:04.3438384Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0
2025-03-04T20:59:04.3441471Z * DOCTEST : /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
2025-03-04T20:59:04.3444595Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0
2025-03-04T20:59:04.3447581Z * 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
2025-03-04T20:59:04.3450608Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0
2025-03-04T20:59:04.3453578Z * 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
2025-03-04T20:59:04.3456627Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0
2025-03-04T20:59:04.3459795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file
2025-03-04T20:59:04.3462981Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0
2025-03-04T20:59:04.3466070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 83 <- wrt source file
2025-03-04T20:59:04.3469184Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0
2025-03-04T20:59:04.3472014Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 122 <- wrt source file
2025-03-04T20:59:04.3474874Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0
2025-03-04T20:59:04.3477336Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 92 <- wrt source file
2025-03-04T20:59:04.3479656Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0
2025-03-04T20:59:04.3482035Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 85 <- wrt source file
2025-03-04T20:59:04.3484436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0
2025-03-04T20:59:04.3487298Z * 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
2025-03-04T20:59:04.3490629Z * 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
2025-03-04T20:59:04.3493500Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file
2025-03-04T20:59:04.3496085Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0
2025-03-04T20:59:04.3498681Z * 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
2025-03-04T20:59:04.3501259Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0
2025-03-04T20:59:04.3503926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file
2025-03-04T20:59:04.3506629Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0
2025-03-04T20:59:04.3509177Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file
2025-03-04T20:59:04.3511676Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0
2025-03-04T20:59:04.3514182Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0, line 1529 <- wrt source file
2025-03-04T20:59:04.3516319Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0
2025-03-04T20:59:04.3518692Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file
2025-03-04T20:59:04.3521291Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0
2025-03-04T20:59:04.3523851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file
2025-03-04T20:59:04.3526514Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0
2025-03-04T20:59:04.3529132Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file
2025-03-04T20:59:04.4171579Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0
2025-03-04T20:59:04.4192950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file
2025-03-04T20:59:04.4202221Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0
2025-03-04T20:59:04.4204880Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file
2025-03-04T20:59:04.4210762Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0
2025-03-04T20:59:04.4213161Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file
2025-03-04T20:59:04.4220534Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0
2025-03-04T20:59:04.4222739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file
2025-03-04T20:59:04.4238009Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0
2025-03-04T20:59:04.4240139Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file
2025-03-04T20:59:04.4244943Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0
2025-03-04T20:59:04.4247129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3466 <- wrt source file
2025-03-04T20:59:04.4253929Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0
2025-03-04T20:59:04.4256228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3538 <- wrt source file
2025-03-04T20:59:04.4260831Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0
2025-03-04T20:59:04.4263318Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3615 <- wrt source file
2025-03-04T20:59:04.4267688Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0
2025-03-04T20:59:04.4269997Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5178 <- wrt source file
2025-03-04T20:59:04.4278466Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0
2025-03-04T20:59:04.4280617Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file
2025-03-04T20:59:04.4286678Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0
2025-03-04T20:59:04.4288745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file
2025-03-04T20:59:04.4292702Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0
2025-03-04T20:59:04.4294781Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file
2025-03-04T20:59:04.4301011Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0
2025-03-04T20:59:04.4303087Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file
2025-03-04T20:59:04.4306544Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0
2025-03-04T20:59:04.4308615Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file
2025-03-04T20:59:04.4341131Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0
2025-03-04T20:59:04.4344030Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file
2025-03-04T20:59:04.4361095Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0
2025-03-04T20:59:04.4363519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file
2025-03-04T20:59:04.4365948Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0
2025-03-04T20:59:04.4367995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file
2025-03-04T20:59:04.4370026Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0
2025-03-04T20:59:04.4372003Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file
2025-03-04T20:59:04.4374163Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0
2025-03-04T20:59:04.4376199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file
2025-03-04T20:59:04.4378504Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0
2025-03-04T20:59:04.4380526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file
2025-03-04T20:59:04.4382572Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0
2025-03-04T20:59:04.4384527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file
2025-03-04T20:59:04.4386502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0
2025-03-04T20:59:04.4388449Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file
2025-03-04T20:59:04.4390430Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0
2025-03-04T20:59:04.4392359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file
2025-03-04T20:59:04.4394384Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0
2025-03-04T20:59:04.4396406Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file
2025-03-04T20:59:04.4398401Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0
2025-03-04T20:59:04.4400439Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file
2025-03-04T20:59:04.4402581Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0
2025-03-04T20:59:04.4404682Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file
2025-03-04T20:59:04.4406808Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0
2025-03-04T20:59:04.4408913Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file
2025-03-04T20:59:04.4411105Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0
2025-03-04T20:59:04.4413450Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file
2025-03-04T20:59:04.4415681Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0
2025-03-04T20:59:04.4417810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file
2025-03-04T20:59:04.4419909Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0
2025-03-04T20:59:04.4421917Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file
2025-03-04T20:59:04.4423929Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0
2025-03-04T20:59:04.4426074Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 104 <- wrt source file
2025-03-04T20:59:04.4428423Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0
2025-03-04T20:59:04.4430703Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file
2025-03-04T20:59:04.4433030Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0
2025-03-04T20:59:04.4435301Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file
2025-03-04T20:59:04.4437651Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0
2025-03-04T20:59:04.4439901Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file
2025-03-04T20:59:04.4442151Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0
2025-03-04T20:59:04.4444359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file
2025-03-04T20:59:04.4446619Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0
2025-03-04T20:59:04.4448919Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file
2025-03-04T20:59:04.4451289Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0
2025-03-04T20:59:04.4453536Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file
2025-03-04T20:59:04.4455801Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0
2025-03-04T20:59:04.4458105Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file
2025-03-04T20:59:04.4460409Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0
2025-03-04T20:59:04.4462718Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file
2025-03-04T20:59:04.4465131Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0
2025-03-04T20:59:04.4467407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file
2025-03-04T20:59:04.4469660Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0
2025-03-04T20:59:04.4471913Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file
2025-03-04T20:59:04.4474310Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0
2025-03-04T20:59:04.4476525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file
2025-03-04T20:59:04.4478781Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0
2025-03-04T20:59:04.4481054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file
2025-03-04T20:59:04.4483409Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0
2025-03-04T20:59:04.4485651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file
2025-03-04T20:59:04.4487891Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0
2025-03-04T20:59:04.4490167Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file
2025-03-04T20:59:04.4492421Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0
2025-03-04T20:59:04.4494630Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file
2025-03-04T20:59:04.4496887Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0
2025-03-04T20:59:04.4499142Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file
2025-03-04T20:59:04.4501368Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0
2025-03-04T20:59:04.4503558Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file
2025-03-04T20:59:04.4505940Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0
2025-03-04T20:59:04.4508571Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file
2025-03-04T20:59:04.4510964Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0
2025-03-04T20:59:04.4513492Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file
2025-03-04T20:59:04.4515863Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0
2025-03-04T20:59:04.4518180Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file
2025-03-04T20:59:04.4520571Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0
2025-03-04T20:59:04.4522876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file
2025-03-04T20:59:04.4525209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0
2025-03-04T20:59:04.4527618Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file
2025-03-04T20:59:04.4530002Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0
2025-03-04T20:59:04.4532453Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1031 <- wrt source file
2025-03-04T20:59:04.4535073Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0
2025-03-04T20:59:04.4537455Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1494 <- wrt source file
2025-03-04T20:59:04.4539788Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0
2025-03-04T20:59:04.4542054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1536 <- wrt source file
2025-03-04T20:59:04.4544383Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0
2025-03-04T20:59:04.4546693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1559 <- wrt source file
2025-03-04T20:59:04.4549133Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0
2025-03-04T20:59:04.4551446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1594 <- wrt source file
2025-03-04T20:59:04.4553768Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0
2025-03-04T20:59:04.4556484Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1652 <- wrt source file
2025-03-04T20:59:04.4559162Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0
2025-03-04T20:59:04.4561761Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1693 <- wrt source file
2025-03-04T20:59:04.4564128Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0
2025-03-04T20:59:04.4566542Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1729 <- wrt source file
2025-03-04T20:59:04.4569267Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0
2025-03-04T20:59:04.4571948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file
2025-03-04T20:59:04.4574810Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0
2025-03-04T20:59:04.4577140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file
2025-03-04T20:59:04.4805881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0
2025-03-04T20:59:04.7432657Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file
2025-03-04T20:59:04.7434644Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0
2025-03-04T20:59:04.7598793Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file
2025-03-04T20:59:04.7621522Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0
2025-03-04T20:59:04.7624092Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 76 <- wrt source file
2025-03-04T20:59:04.7626475Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0
2025-03-04T20:59:04.7628735Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 282 <- wrt source file
2025-03-04T20:59:04.7631014Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0
2025-03-04T20:59:04.7633532Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 464 <- wrt source file
2025-03-04T20:59:04.7636246Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0
2025-03-04T20:59:04.7638979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 596 <- wrt source file
2025-03-04T20:59:04.7642196Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0
2025-03-04T20:59:04.7644619Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 748 <- wrt source file
2025-03-04T20:59:04.7647519Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0
2025-03-04T20:59:04.7650241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file
2025-03-04T20:59:04.7652993Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0
2025-03-04T20:59:04.7655851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file
2025-03-04T20:59:04.7658773Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0
2025-03-04T20:59:04.7661795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file
2025-03-04T20:59:04.7664517Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0
2025-03-04T20:59:04.7667108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file
2025-03-04T20:59:04.7669579Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0
2025-03-04T20:59:04.7671978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file
2025-03-04T20:59:04.7692284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0
2025-03-04T20:59:04.7694937Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file
2025-03-04T20:59:04.7771158Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0
2025-03-04T20:59:04.7774119Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file
2025-03-04T20:59:04.7776682Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0
2025-03-04T20:59:04.7779178Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file
2025-03-04T20:59:04.7853838Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0
2025-03-04T20:59:04.7856601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file
2025-03-04T20:59:04.7859519Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0
2025-03-04T20:59:04.7861659Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file
2025-03-04T20:59:04.7865902Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0
2025-03-04T20:59:04.7868007Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file
2025-03-04T20:59:04.7880808Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0
2025-03-04T20:59:04.7883152Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file
2025-03-04T20:59:04.7894682Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0
2025-03-04T20:59:04.7897207Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file
2025-03-04T20:59:04.8086049Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0
2025-03-04T20:59:04.8088924Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file
2025-03-04T20:59:05.0661516Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0
2025-03-04T20:59:05.0825133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file
2025-03-04T20:59:05.0827734Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0
2025-03-04T20:59:05.0829991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file
2025-03-04T20:59:05.0834468Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0
2025-03-04T20:59:05.0836647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file
2025-03-04T20:59:05.0844186Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0
2025-03-04T20:59:05.0846446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file
2025-03-04T20:59:05.0864509Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0
2025-03-04T20:59:05.0866685Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file
2025-03-04T20:59:05.0873194Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0
2025-03-04T20:59:05.0875672Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file
2025-03-04T20:59:05.0899473Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0
2025-03-04T20:59:05.0902272Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file
2025-03-04T20:59:05.0907425Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0
2025-03-04T20:59:05.0909779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file
2025-03-04T20:59:05.0922654Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0
2025-03-04T20:59:05.0924945Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 519 <- wrt source file
2025-03-04T20:59:05.0931561Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0
2025-03-04T20:59:05.0934276Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 597 <- wrt source file
2025-03-04T20:59:05.0938247Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0
2025-03-04T20:59:05.0940403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 679 <- wrt source file
2025-03-04T20:59:05.0945328Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0
2025-03-04T20:59:05.0947620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 750 <- wrt source file
2025-03-04T20:59:05.0958021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0
2025-03-04T20:59:05.0960820Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 943 <- wrt source file
2025-03-04T20:59:05.0966609Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0
2025-03-04T20:59:05.0969472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1265 <- wrt source file
2025-03-04T20:59:05.0975582Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0
2025-03-04T20:59:05.0978078Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1405 <- wrt source file
2025-03-04T20:59:05.0986203Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0
2025-03-04T20:59:05.0988631Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1470 <- wrt source file
2025-03-04T20:59:05.0994065Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0
2025-03-04T20:59:05.0996717Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1549 <- wrt source file
2025-03-04T20:59:05.1003831Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0
2025-03-04T20:59:05.1006451Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1649 <- wrt source file
2025-03-04T20:59:05.1017395Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0
2025-03-04T20:59:05.1019749Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1890 <- wrt source file
2025-03-04T20:59:05.1050845Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0
2025-03-04T20:59:05.1053296Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 538 <- wrt source file
2025-03-04T20:59:05.1055911Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0
2025-03-04T20:59:05.1058369Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1020 <- wrt source file
2025-03-04T20:59:05.1065455Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0
2025-03-04T20:59:05.1067876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1274 <- wrt source file
2025-03-04T20:59:05.1072710Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0
2025-03-04T20:59:05.1075487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2192 <- wrt source file
2025-03-04T20:59:05.1077952Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0
2025-03-04T20:59:05.1080367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2634 <- wrt source file
2025-03-04T20:59:05.1082812Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0
2025-03-04T20:59:05.1085267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2662 <- wrt source file
2025-03-04T20:59:05.1088009Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0
2025-03-04T20:59:05.1090511Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2689 <- wrt source file
2025-03-04T20:59:05.1092882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0
2025-03-04T20:59:05.1095267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2716 <- wrt source file
2025-03-04T20:59:05.1097813Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0
2025-03-04T20:59:05.1100268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2747 <- wrt source file
2025-03-04T20:59:05.1102766Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0
2025-03-04T20:59:05.1105159Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2771 <- wrt source file
2025-03-04T20:59:05.1107529Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0
2025-03-04T20:59:05.1110054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2809 <- wrt source file
2025-03-04T20:59:05.1112990Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0
2025-03-04T20:59:05.1115526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file
2025-03-04T20:59:05.1130528Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0
2025-03-04T20:59:05.1133857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file
2025-03-04T20:59:05.1140916Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0
2025-03-04T20:59:05.1144106Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file
2025-03-04T20:59:05.1148908Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0
2025-03-04T20:59:05.1151410Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file
2025-03-04T20:59:05.1155048Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0
2025-03-04T20:59:05.1157390Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file
2025-03-04T20:59:05.1162482Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0
2025-03-04T20:59:05.1164814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file
2025-03-04T20:59:05.1183949Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0
2025-03-04T20:59:05.1186342Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file
2025-03-04T20:59:05.7642159Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0
2025-03-04T20:59:05.7941280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file
2025-03-04T20:59:05.7951627Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0
2025-03-04T20:59:05.7953991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file
2025-03-04T20:59:05.7959594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0
2025-03-04T20:59:05.7961948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file
2025-03-04T20:59:05.7986380Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0
2025-03-04T20:59:05.7988596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file
2025-03-04T20:59:05.7994630Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0
2025-03-04T20:59:05.8002011Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file
2025-03-04T20:59:05.8004326Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0
2025-03-04T20:59:05.8006792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file
2025-03-04T20:59:05.8009156Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0
2025-03-04T20:59:05.8011514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file
2025-03-04T20:59:05.8014755Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0
2025-03-04T20:59:05.8017080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file
2025-03-04T20:59:05.8023166Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0
2025-03-04T20:59:05.8025815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file
2025-03-04T20:59:06.3460468Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0
2025-03-04T20:59:06.3758759Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file
2025-03-04T20:59:06.3769147Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0
2025-03-04T20:59:06.3771294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file
2025-03-04T20:59:06.3777561Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0
2025-03-04T20:59:06.3779902Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file
2025-03-04T20:59:06.3803930Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0
2025-03-04T20:59:06.3806489Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file
2025-03-04T20:59:06.3811776Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0
2025-03-04T20:59:06.3814436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file
2025-03-04T20:59:06.3819771Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0
2025-03-04T20:59:06.3822359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file
2025-03-04T20:59:06.3828139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0
2025-03-04T20:59:06.3830934Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file
2025-03-04T20:59:06.3884025Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0
2025-03-04T20:59:06.3886514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file
2025-03-04T20:59:06.6220824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0
2025-03-04T20:59:06.6279313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file
2025-03-04T20:59:06.6291965Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0
2025-03-04T20:59:06.6294530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file
2025-03-04T20:59:06.7136729Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0
2025-03-04T20:59:06.7138958Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file
2025-03-04T20:59:06.7147928Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0
2025-03-04T20:59:06.7150219Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file
2025-03-04T20:59:06.7192113Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0
2025-03-04T20:59:06.7194374Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file
2025-03-04T20:59:06.8910749Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0
2025-03-04T20:59:06.8970595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file
2025-03-04T20:59:06.9022739Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0
2025-03-04T20:59:06.9025123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file
2025-03-04T20:59:06.9830935Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0
2025-03-04T20:59:06.9833644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file
2025-03-04T20:59:06.9841867Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0
2025-03-04T20:59:06.9844443Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file
2025-03-04T20:59:06.9898283Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0
2025-03-04T20:59:06.9901203Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file
2025-03-04T20:59:07.2185824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0
2025-03-04T20:59:07.2243570Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file
2025-03-04T20:59:07.2250058Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0
2025-03-04T20:59:07.2252533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file
2025-03-04T20:59:07.2260756Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0
2025-03-04T20:59:07.2263241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file
2025-03-04T20:59:07.2293159Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0
2025-03-04T20:59:07.2359592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file
2025-03-04T20:59:07.2364074Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0
2025-03-04T20:59:07.2366531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file
2025-03-04T20:59:07.2372538Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0
2025-03-04T20:59:07.2375132Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file
2025-03-04T20:59:07.2396812Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0
2025-03-04T20:59:07.2399133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 591 <- wrt source file
2025-03-04T20:59:07.2408532Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0
2025-03-04T20:59:07.2410575Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 948 <- wrt source file
2025-03-04T20:59:07.2750047Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0
2025-03-04T20:59:07.2752605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file
2025-03-04T20:59:07.2767316Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0
2025-03-04T20:59:07.2769649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1536 <- wrt source file
2025-03-04T20:59:07.2780105Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0
2025-03-04T20:59:07.2782323Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1658 <- wrt source file
2025-03-04T20:59:07.2790647Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0
2025-03-04T20:59:07.2793681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1772 <- wrt source file
2025-03-04T20:59:07.2805422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0
2025-03-04T20:59:07.2807702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file
2025-03-04T20:59:07.2820567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0
2025-03-04T20:59:07.2822982Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file
2025-03-04T20:59:07.2826020Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0
2025-03-04T20:59:07.2828760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file
2025-03-04T20:59:07.2833237Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0
2025-03-04T20:59:07.2835999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 88 <- wrt source file
2025-03-04T20:59:08.1528086Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0
2025-03-04T20:59:08.1546248Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 256 <- wrt source file
2025-03-04T20:59:08.1548978Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0
2025-03-04T20:59:08.1551543Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 326 <- wrt source file
2025-03-04T20:59:08.2706647Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0
2025-03-04T20:59:08.2713077Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 544 <- wrt source file
2025-03-04T20:59:08.5235422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0
2025-03-04T20:59:08.5243993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 667 <- wrt source file
2025-03-04T20:59:08.5568678Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0
2025-03-04T20:59:08.5605924Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 973 <- wrt source file
2025-03-04T20:59:08.6183048Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0
2025-03-04T20:59:08.6186095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file
2025-03-04T20:59:08.6208550Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0
2025-03-04T20:59:08.6211032Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file
2025-03-04T20:59:08.6220783Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0
2025-03-04T20:59:08.6223411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file
2025-03-04T20:59:08.6229361Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0
2025-03-04T20:59:08.6231933Z * 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
2025-03-04T20:59:08.6234526Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0
2025-03-04T20:59:08.6237163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 625 <- wrt source file
2025-03-04T20:59:08.6240162Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0
2025-03-04T20:59:08.6242981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1423 <- wrt source file
2025-03-04T20:59:08.6245906Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0
2025-03-04T20:59:08.6248896Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1975 <- wrt source file
2025-03-04T20:59:08.6252062Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0
2025-03-04T20:59:08.6255140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1985 <- wrt source file
2025-03-04T20:59:08.6258364Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1
2025-03-04T20:59:08.6261675Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2020 <- wrt source file
2025-03-04T20:59:08.6264966Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0
2025-03-04T20:59:08.6267825Z * 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
2025-03-04T20:59:08.6270494Z * 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
2025-03-04T20:59:08.6272883Z * 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
2025-03-04T20:59:08.6275244Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0
2025-03-04T20:59:08.6277977Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file
2025-03-04T20:59:08.6280483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0
2025-03-04T20:59:08.6282940Z * 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
2025-03-04T20:59:08.6285454Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0
2025-03-04T20:59:08.6288036Z * 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
2025-03-04T20:59:08.6290607Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0
2025-03-04T20:59:08.6293171Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 505 <- wrt source file
2025-03-04T20:59:08.6295838Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0
2025-03-04T20:59:08.6298248Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 844 <- wrt source file
2025-03-04T20:59:08.6300518Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0
2025-03-04T20:59:08.6302768Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 880 <- wrt source file
2025-03-04T20:59:08.6305169Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0
2025-03-04T20:59:08.6307477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 923 <- wrt source file
2025-03-04T20:59:08.6309782Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0
2025-03-04T20:59:08.6311943Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1190 <- wrt source file
2025-03-04T20:59:08.6314106Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0
2025-03-04T20:59:08.6316227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1218 <- wrt source file
2025-03-04T20:59:08.6318523Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0
2025-03-04T20:59:08.6320745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 354 <- wrt source file
2025-03-04T20:59:08.6323031Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0
2025-03-04T20:59:08.6325249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 432 <- wrt source file
2025-03-04T20:59:08.6327450Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0
2025-03-04T20:59:08.6329626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 490 <- wrt source file
2025-03-04T20:59:08.6331870Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0
2025-03-04T20:59:08.6334111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 546 <- wrt source file
2025-03-04T20:59:08.6339680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0
2025-03-04T20:59:08.6341884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 574 <- wrt source file
2025-03-04T20:59:08.6356504Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0
2025-03-04T20:59:08.6358689Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file
2025-03-04T20:59:08.6365332Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0
2025-03-04T20:59:08.6367840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file
2025-03-04T20:59:08.6372570Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0
2025-03-04T20:59:08.6375237Z * 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
2025-03-04T20:59:08.6377911Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0
2025-03-04T20:59:08.6380247Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file
2025-03-04T20:59:08.6383988Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0
2025-03-04T20:59:08.6386367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file
2025-03-04T20:59:08.6389499Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0
2025-03-04T20:59:08.6391998Z * 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
2025-03-04T20:59:08.6394611Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0
2025-03-04T20:59:08.6397474Z * 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
2025-03-04T20:59:08.6479107Z * 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
2025-03-04T20:59:08.6481864Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 306 <- wrt source file
2025-03-04T20:59:08.6484125Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0
2025-03-04T20:59:08.6486490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 408 <- wrt source file
2025-03-04T20:59:08.6489020Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0
2025-03-04T20:59:08.6491329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 508 <- wrt source file
2025-03-04T20:59:08.6493624Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0
2025-03-04T20:59:08.6496236Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 568 <- wrt source file
2025-03-04T20:59:08.6498725Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0
2025-03-04T20:59:08.6501226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 633 <- wrt source file
2025-03-04T20:59:08.6503583Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0
2025-03-04T20:59:08.6505831Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 711 <- wrt source file
2025-03-04T20:59:08.6508134Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0
2025-03-04T20:59:08.6510386Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 844 <- wrt source file
2025-03-04T20:59:08.6512642Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0
2025-03-04T20:59:08.6514930Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 993 <- wrt source file
2025-03-04T20:59:08.6517417Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0
2025-03-04T20:59:08.6519494Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1149 <- wrt source file
2025-03-04T20:59:08.6521835Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0
2025-03-04T20:59:08.6524146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1292 <- wrt source file
2025-03-04T20:59:08.6526435Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0
2025-03-04T20:59:08.6528720Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1540 <- wrt source file
2025-03-04T20:59:08.6530970Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0
2025-03-04T20:59:08.6533475Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1810 <- wrt source file
2025-03-04T20:59:08.6536488Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0
2025-03-04T20:59:08.6539218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1826 <- wrt source file
2025-03-04T20:59:08.6542062Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1
2025-03-04T20:59:08.6544577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1970 <- wrt source file
2025-03-04T20:59:08.6546936Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0
2025-03-04T20:59:08.6549076Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 331 <- wrt source file
2025-03-04T20:59:08.6551465Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0
2025-03-04T20:59:08.6553579Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file
2025-03-04T20:59:08.6555866Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0
2025-03-04T20:59:08.6558483Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 281 <- wrt source file
2025-03-04T20:59:08.6561392Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0
2025-03-04T20:59:08.6563936Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 598 <- wrt source file
2025-03-04T20:59:08.6566190Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0
2025-03-04T20:59:08.6568560Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 338 <- wrt source file
2025-03-04T20:59:08.6571306Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0
2025-03-04T20:59:08.6574073Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file
2025-03-04T20:59:08.6576491Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0
2025-03-04T20:59:08.6579032Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 614 <- wrt source file
2025-03-04T20:59:08.6581694Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0
2025-03-04T20:59:08.6584313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 735 <- wrt source file
2025-03-04T20:59:08.6587030Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0
2025-03-04T20:59:08.6589789Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 824 <- wrt source file
2025-03-04T20:59:08.6592418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0
2025-03-04T20:59:08.6595255Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4651 <- wrt source file
2025-03-04T20:59:08.6598090Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0
2025-03-04T20:59:08.6600569Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4665 <- wrt source file
2025-03-04T20:59:08.6603475Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0
2025-03-04T20:59:08.6606373Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4695 <- wrt source file
2025-03-04T20:59:08.6609319Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0
2025-03-04T20:59:08.6612100Z * 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
2025-03-04T20:59:08.6615035Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0
2025-03-04T20:59:08.6617920Z * 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
2025-03-04T20:59:08.6620842Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0
2025-03-04T20:59:08.6623857Z * 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 308 <- wrt source file
2025-03-04T20:59:08.6627097Z * 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
2025-03-04T20:59:08.6630176Z * 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
2025-03-04T20:59:08.6633002Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0
2025-03-04T20:59:08.6635674Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 247 <- wrt source file
2025-03-04T20:59:08.6637962Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0
2025-03-04T20:59:08.6640084Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 290 <- wrt source file
2025-03-04T20:59:08.6642283Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0
2025-03-04T20:59:08.6644490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 327 <- wrt source file
2025-03-04T20:59:08.6646825Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0
2025-03-04T20:59:08.6649070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 357 <- wrt source file
2025-03-04T20:59:08.6650907Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0
2025-03-04T20:59:08.6652223Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 392 <- wrt source file
2025-03-04T20:59:08.6653472Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0
2025-03-04T20:59:08.6654705Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 427 <- wrt source file
2025-03-04T20:59:08.6655976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0
2025-03-04T20:59:08.6657828Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 464 <- wrt source file
2025-03-04T20:59:08.6659070Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0
2025-03-04T20:59:08.6660304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 880 <- wrt source file
2025-03-04T20:59:08.6661684Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0
2025-03-04T20:59:08.6662984Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 268 <- wrt source file
2025-03-04T20:59:08.6665089Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0
2025-03-04T20:59:08.6667856Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 310 <- wrt source file
2025-03-04T20:59:08.6670802Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0
2025-03-04T20:59:08.6673529Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1079 <- wrt source file
2025-03-04T20:59:08.6676025Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0
2025-03-04T20:59:08.6677945Z * 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
2025-03-04T20:59:08.6680058Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0
2025-03-04T20:59:08.6682435Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file
2025-03-04T20:59:08.6684944Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0
2025-03-04T20:59:08.6687195Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file
2025-03-04T20:59:08.6689366Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0
2025-03-04T20:59:08.6691518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 547 <- wrt source file
2025-03-04T20:59:08.6693595Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0
2025-03-04T20:59:08.6695462Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 749 <- wrt source file
2025-03-04T20:59:08.6697619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0
2025-03-04T20:59:08.6699906Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file
2025-03-04T20:59:08.6701812Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0
2025-03-04T20:59:08.6704195Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 713 <- wrt source file
2025-03-04T20:59:08.7108153Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0
2025-03-04T20:59:08.7110714Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file
2025-03-04T20:59:08.7113339Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0
2025-03-04T20:59:08.7115876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file
2025-03-04T20:59:08.7118117Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0
2025-03-04T20:59:08.7119948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0, line 441 <- wrt source file
2025-03-04T20:59:08.7122174Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0
2025-03-04T20:59:08.7124395Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::Sampler:0, line 34 <- wrt source file
2025-03-04T20:59:08.7126629Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::Sampler:0
2025-03-04T20:59:08.7129113Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 232 <- wrt source file
2025-03-04T20:59:08.7131782Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0
2025-03-04T20:59:08.7134166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 295 <- wrt source file
2025-03-04T20:59:08.7136538Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0
2025-03-04T20:59:08.7139283Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file
2025-03-04T20:59:08.7141865Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0
2025-03-04T20:59:08.7144314Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file
2025-03-04T20:59:08.7146795Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0
2025-03-04T20:59:08.7149253Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file
2025-03-04T20:59:08.7151717Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0
2025-03-04T20:59:08.7154134Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 97 <- wrt source file
2025-03-04T20:59:08.7157011Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0
2025-03-04T20:59:08.7159703Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 264 <- wrt source file
2025-03-04T20:59:08.7162345Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0
2025-03-04T20:59:08.7165192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 52 <- wrt source file
2025-03-04T20:59:08.7168185Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0
2025-03-04T20:59:08.7171163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 198 <- wrt source file
2025-03-04T20:59:08.7174409Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0
2025-03-04T20:59:08.7177810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 88 <- wrt source file
2025-03-04T20:59:08.7180996Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0
2025-03-04T20:59:08.7183893Z * 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
2025-03-04T20:59:08.7186930Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0
2025-03-04T20:59:08.7189854Z * 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
2025-03-04T20:59:08.7192804Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0
2025-03-04T20:59:08.7195670Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 307 <- wrt source file
2025-03-04T20:59:08.7198507Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0
2025-03-04T20:59:08.7201566Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 393 <- wrt source file
2025-03-04T20:59:08.7204722Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0
2025-03-04T20:59:08.7207755Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 603 <- wrt source file
2025-03-04T20:59:08.7210814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0
2025-03-04T20:59:08.7213744Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 671 <- wrt source file
2025-03-04T20:59:08.7216555Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0
2025-03-04T20:59:08.7219552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 31 <- wrt source file
2025-03-04T20:59:08.7222769Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0
2025-03-04T20:59:08.7225822Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 35 <- wrt source file
2025-03-04T20:59:08.7228923Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0
2025-03-04T20:59:08.7231912Z * 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
2025-03-04T20:59:08.7234902Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0
2025-03-04T20:59:08.7237825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 113 <- wrt source file
2025-03-04T20:59:08.7240845Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0
2025-03-04T20:59:08.7243458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 180 <- wrt source file
2025-03-04T20:59:08.7245804Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0
2025-03-04T20:59:08.7247385Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 37 <- wrt source file
2025-03-04T20:59:08.7248977Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0
2025-03-04T20:59:08.7250610Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file
2025-03-04T20:59:08.7252765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0
2025-03-04T20:59:08.7254431Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file
2025-03-04T20:59:08.7256173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0
2025-03-04T20:59:08.7257837Z * 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
2025-03-04T20:59:08.7260242Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0
2025-03-04T20:59:08.7262894Z * 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
2025-03-04T20:59:08.7265622Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0
2025-03-04T20:59:08.7268222Z * 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
2025-03-04T20:59:08.7270895Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0
2025-03-04T20:59:08.7273557Z * 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
2025-03-04T20:59:08.7276294Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0
2025-03-04T20:59:08.7278867Z * 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
2025-03-04T20:59:08.7281503Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0
2025-03-04T20:59:08.7284086Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file
2025-03-04T20:59:08.7286790Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0
2025-03-04T20:59:08.7289424Z * 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
2025-03-04T20:59:08.7336086Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0
2025-03-04T20:59:08.7338872Z * 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
2025-03-04T20:59:08.7341367Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0
2025-03-04T20:59:08.7343722Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 440 <- wrt source file
2025-03-04T20:59:08.7347353Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0
2025-03-04T20:59:08.7349748Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 536 <- wrt source file
2025-03-04T20:59:08.7352241Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0
2025-03-04T20:59:08.7354601Z * 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
2025-03-04T20:59:08.7359485Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0
2025-03-04T20:59:08.7361825Z * 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
2025-03-04T20:59:08.7364284Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0
2025-03-04T20:59:08.7366689Z * 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
2025-03-04T20:59:08.7369098Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0
2025-03-04T20:59:08.7371507Z * 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
2025-03-04T20:59:08.7374145Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0
2025-03-04T20:59:08.7376609Z * 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
2025-03-04T20:59:08.7379159Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0
2025-03-04T20:59:08.7381614Z * 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
2025-03-04T20:59:08.7384102Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0
2025-03-04T20:59:08.7386633Z * 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
2025-03-04T20:59:08.7389282Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0
2025-03-04T20:59:08.7391882Z * 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
2025-03-04T20:59:08.7394651Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0
2025-03-04T20:59:08.7397225Z * 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
2025-03-04T20:59:08.7399947Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0
2025-03-04T20:59:08.7402578Z * 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
2025-03-04T20:59:08.7405356Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0
2025-03-04T20:59:08.7407646Z * 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
2025-03-04T20:59:08.7409904Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0
2025-03-04T20:59:08.7412283Z * 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
2025-03-04T20:59:08.7414554Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0
2025-03-04T20:59:08.7416939Z * 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
2025-03-04T20:59:08.7419619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0
2025-03-04T20:59:08.7422124Z * 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
2025-03-04T20:59:08.7424649Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0
2025-03-04T20:59:08.7427057Z * 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
2025-03-04T20:59:08.7429481Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0
2025-03-04T20:59:08.7431710Z * 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
2025-03-04T20:59:08.7433906Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0
2025-03-04T20:59:08.7435022Z ============
2025-03-04T20:59:08.7435429Z Finished doctests
2025-03-04T20:59:08.7435790Z 342 / 709 passed
2025-03-04T20:59:08.7436160Z 
2025-03-04T20:59:08.7436632Z === Found 116 parse-time warnings ===
2025-03-04T20:59:08.7437284Z --- Parse Warning: 1 / 116 ---
2025-03-04T20:59:08.7438928Z     /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=1507.
2025-03-04T20:59:08.7440795Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7441592Z     
2025-03-04T20:59:08.7441968Z     dim_order(ambiguity_check=False) -> tuple
2025-03-04T20:59:08.7442455Z     
2025-03-04T20:59:08.7443043Z     Returns the uniquely determined tuple of int describing the dim order or
2025-03-04T20:59:08.7443792Z     physical layout of :attr:`self`.
2025-03-04T20:59:08.7444256Z     
2025-03-04T20:59:08.7444832Z     The dim order represents how dimensions are laid out in memory of dense tensors,
2025-03-04T20:59:08.7445673Z     starting from the outermost to the innermost dimension.
2025-03-04T20:59:08.7446243Z     
2025-03-04T20:59:08.7446720Z     Note that the dim order may not always be uniquely determined.
2025-03-04T20:59:08.7447776Z     If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
2025-03-04T20:59:08.7449169Z     If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted
2025-03-04T20:59:08.7450344Z     into exactly one of the given memory formats, or it cannot be uniquely determined.
2025-03-04T20:59:08.7451420Z     If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness.
2025-03-04T20:59:08.7452290Z     Otherwise, it will raise TypeError.
2025-03-04T20:59:08.7452816Z     
2025-03-04T20:59:08.7453107Z     Args:
2025-03-04T20:59:08.7453869Z         ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order.
2025-03-04T20:59:08.7454782Z     
2025-03-04T20:59:08.7455170Z     Examples::
2025-03-04T20:59:08.7455661Z     
2025-03-04T20:59:08.7456078Z         >>> torch.empty((2, 3, 5, 7)).dim_order()
2025-03-04T20:59:08.7456655Z         (0, 1, 2, 3)
2025-03-04T20:59:08.7457205Z         >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order()
2025-03-04T20:59:08.7457962Z         (0, 2, 1, 3)
2025-03-04T20:59:08.7458600Z         >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order()
2025-03-04T20:59:08.7459245Z         (0, 2, 3, 1)
2025-03-04T20:59:08.7459635Z         >>> torch.empty((1, 2, 3, 4)).dim_order()
2025-03-04T20:59:08.7460135Z         (0, 1, 2, 3)
2025-03-04T20:59:08.7460542Z         >>> try:
2025-03-04T20:59:08.7461029Z         ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True)
2025-03-04T20:59:08.7461726Z         ... except RuntimeError as e:
2025-03-04T20:59:08.7462266Z         ...     print(e)
2025-03-04T20:59:08.7463110Z         The tensor does not have unique dim order, or cannot map to exact one of the given memory formats.
2025-03-04T20:59:08.7464097Z         >>> torch.empty((1, 2, 3, 4)).dim_order(
2025-03-04T20:59:08.7464861Z         ...     ambiguity_check=[torch.contiguous_format, torch.channels_last]
2025-03-04T20:59:08.7465671Z         ... )  # It can be mapped to contiguous format
2025-03-04T20:59:08.7466341Z         (0, 1, 2, 3)
2025-03-04T20:59:08.7466758Z         >>> try:
2025-03-04T20:59:08.7467332Z         ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL")
2025-03-04T20:59:08.7468058Z         ... except TypeError as e:
2025-03-04T20:59:08.7468535Z         ...     print(e)
2025-03-04T20:59:08.7469209Z         The ambiguity_check argument must be a bool or a list of memory formats.
2025-03-04T20:59:08.7469905Z     
2025-03-04T20:59:08.7470275Z     .. warning::
2025-03-04T20:59:08.7470853Z         The dim_order tensor API is experimental and subject to change.
2025-03-04T20:59:08.7471416Z     
2025-03-04T20:59:08.7472061Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7472871Z     
2025-03-04T20:59:08.7473255Z       warnings.warn(msg)
2025-03-04T20:59:08.7473844Z     
2025-03-04T20:59:08.7474338Z --- Parse Warning: 2 / 116 ---
2025-03-04T20:59:08.7476134Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=432.
2025-03-04T20:59:08.7478439Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7479496Z     Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2025-03-04T20:59:08.7480127Z     
2025-03-04T20:59:08.7480545Z             This is helpful when you want to visualize data over some
2025-03-04T20:59:08.7481181Z             range of inputs. See below for a plotting example.
2025-03-04T20:59:08.7481785Z     
2025-03-04T20:59:08.7482278Z             Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2025-03-04T20:59:08.7483111Z             inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2025-03-04T20:59:08.7483961Z             this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2025-03-04T20:59:08.7484787Z             G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2025-03-04T20:59:08.7485604Z             the output :math:`G_i` is constructed by expanding :math:`T_i`
2025-03-04T20:59:08.7486326Z             to the result shape.
2025-03-04T20:59:08.7486812Z     
2025-03-04T20:59:08.7487178Z             .. note::
2025-03-04T20:59:08.7487725Z                 0D inputs are treated equivalently to 1D inputs of a
2025-03-04T20:59:08.7488389Z                 single element.
2025-03-04T20:59:08.7488858Z     
2025-03-04T20:59:08.7489218Z             .. warning::
2025-03-04T20:59:08.7489744Z                 `torch.meshgrid(*tensors)` currently has the same behavior
2025-03-04T20:59:08.7490394Z                 as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2025-03-04T20:59:08.7490914Z     
2025-03-04T20:59:08.7491377Z                 In the future `torch.meshgrid` will transition to
2025-03-04T20:59:08.7491964Z                 `indexing='xy'` as the default.
2025-03-04T20:59:08.7492511Z     
2025-03-04T20:59:08.7493025Z                 https://github.com/pytorch/pytorch/issues/50276 tracks
2025-03-04T20:59:08.7493857Z                 this issue with the goal of migrating to NumPy's behavior.
2025-03-04T20:59:08.7494542Z     
2025-03-04T20:59:08.7494882Z             .. seealso::
2025-03-04T20:59:08.7495275Z     
2025-03-04T20:59:08.7495784Z                 :func:`torch.cartesian_prod` has the same effect but it
2025-03-04T20:59:08.7496549Z                 collects the data in a tensor of vectors.
2025-03-04T20:59:08.7497155Z     
2025-03-04T20:59:08.7497521Z             Args:
2025-03-04T20:59:08.7498335Z                 tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2025-03-04T20:59:08.7499357Z                     treated as tensors of size :math:`(1,)` automatically
2025-03-04T20:59:08.7500045Z     
2025-03-04T20:59:08.7500556Z                 indexing: (str, optional): the indexing mode, either "xy"
2025-03-04T20:59:08.7501191Z                     or "ij", defaults to "ij". See warning for future changes.
2025-03-04T20:59:08.7501833Z     
2025-03-04T20:59:08.7502228Z                     If "xy" is selected, the first dimension corresponds
2025-03-04T20:59:08.7502879Z                     to the cardinality of the second input and the second
2025-03-04T20:59:08.7503680Z                     dimension corresponds to the cardinality of the first
2025-03-04T20:59:08.7504393Z                     input.
2025-03-04T20:59:08.7504852Z     
2025-03-04T20:59:08.7505331Z                     If "ij" is selected, the dimensions are in the same
2025-03-04T20:59:08.7506057Z                     order as the cardinality of the inputs.
2025-03-04T20:59:08.7506665Z     
2025-03-04T20:59:08.7507029Z             Returns:
2025-03-04T20:59:08.7507613Z                 seq (sequence of Tensors): If the input has :math:`N`
2025-03-04T20:59:08.7508401Z                 tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2025-03-04T20:59:08.7509238Z                 output will also have :math:`N` tensors, where each tensor
2025-03-04T20:59:08.7510021Z                 is of shape :math:`(S_0, ..., S_{N-1})`.
2025-03-04T20:59:08.7510577Z     
2025-03-04T20:59:08.7510978Z             Example::
2025-03-04T20:59:08.7511332Z     
2025-03-04T20:59:08.7511652Z                 >>> x = torch.tensor([1, 2, 3])
2025-03-04T20:59:08.7512151Z                 >>> y = torch.tensor([4, 5, 6])
2025-03-04T20:59:08.7512607Z     
2025-03-04T20:59:08.7513190Z                 Observe the element-wise pairings across the grid, (1, 4),
2025-03-04T20:59:08.7514012Z                 (1, 5), ..., (3, 6). This is the same thing as the
2025-03-04T20:59:08.7514653Z                 cartesian product.
2025-03-04T20:59:08.7515308Z                 >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2025-03-04T20:59:08.7515946Z                 >>> grid_x
2025-03-04T20:59:08.7516348Z                 tensor([[1, 1, 1],
2025-03-04T20:59:08.7516765Z                         [2, 2, 2],
2025-03-04T20:59:08.7517185Z                         [3, 3, 3]])
2025-03-04T20:59:08.7517715Z                 >>> grid_y
2025-03-04T20:59:08.7518198Z                 tensor([[4, 5, 6],
2025-03-04T20:59:08.7518726Z                         [4, 5, 6],
2025-03-04T20:59:08.7519254Z                         [4, 5, 6]])
2025-03-04T20:59:08.7519765Z     
2025-03-04T20:59:08.7520213Z                 This correspondence can be seen when these grids are
2025-03-04T20:59:08.7520745Z                 stacked properly.
2025-03-04T20:59:08.7521164Z                 >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2025-03-04T20:59:08.7521635Z                 ...             torch.cartesian_prod(x, y))
2025-03-04T20:59:08.7521987Z                 True
2025-03-04T20:59:08.7522236Z     
2025-03-04T20:59:08.7522629Z                 `torch.meshgrid` is commonly used to produce a grid for
2025-03-04T20:59:08.7523143Z                 plotting.
2025-03-04T20:59:08.7523549Z                 >>> # xdoctest: +REQUIRES(module:matplotlib)
2025-03-04T20:59:08.7523951Z                 >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2025-03-04T20:59:08.7524343Z                 >>> import matplotlib.pyplot as plt
2025-03-04T20:59:08.7524733Z                 >>> xs = torch.linspace(-5, 5, steps=100)
2025-03-04T20:59:08.7525119Z                 >>> ys = torch.linspace(-5, 5, steps=100)
2025-03-04T20:59:08.7525516Z                 >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2025-03-04T20:59:08.7525924Z                 >>> z = torch.sin(torch.sqrt(x * x + y * y))
2025-03-04T20:59:08.7526305Z                 >>> ax = plt.axes(projection='3d')
2025-03-04T20:59:08.7526716Z                 >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2025-03-04T20:59:08.7527100Z                 >>> plt.show()
2025-03-04T20:59:08.7527387Z     
2025-03-04T20:59:08.7527660Z             .. image:: ../_static/img/meshgrid.png
2025-03-04T20:59:08.7528015Z                 :width: 512
2025-03-04T20:59:08.7528289Z     
2025-03-04T20:59:08.7528507Z             
2025-03-04T20:59:08.7528922Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7529460Z     
2025-03-04T20:59:08.7529699Z       warnings.warn(msg)
2025-03-04T20:59:08.7529979Z     
2025-03-04T20:59:08.7530363Z --- Parse Warning: 3 / 116 ---
2025-03-04T20:59:08.7531490Z     /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=828.
2025-03-04T20:59:08.7532741Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7533540Z     unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
2025-03-04T20:59:08.7534148Z     
2025-03-04T20:59:08.7534440Z         Returns the unique elements of the input tensor.
2025-03-04T20:59:08.7534808Z     
2025-03-04T20:59:08.7535233Z         .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2025-03-04T20:59:08.7535878Z             this function also eliminates non-consecutive duplicate values.
2025-03-04T20:59:08.7536299Z     
2025-03-04T20:59:08.7536700Z         .. note:: Currently in the CUDA implementation and the CPU implementation,
2025-03-04T20:59:08.7537355Z             `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2025-03-04T20:59:08.7538199Z             Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2025-03-04T20:59:08.7538794Z             :func:`torch.unique_consecutive` which avoids the sorting.
2025-03-04T20:59:08.7539192Z     
2025-03-04T20:59:08.7539409Z         Args:
2025-03-04T20:59:08.7539661Z             input (Tensor): the input tensor
2025-03-04T20:59:08.7540124Z             sorted (bool): Whether to sort the unique elements in ascending order
2025-03-04T20:59:08.7540590Z                 before returning as output.
2025-03-04T20:59:08.7541039Z             return_inverse (bool): Whether to also return the indices for where
2025-03-04T20:59:08.7541617Z                 elements in the original input ended up in the returned unique list.
2025-03-04T20:59:08.7542206Z             return_counts (bool): Whether to also return the counts for each unique
2025-03-04T20:59:08.7542656Z                 element.
2025-03-04T20:59:08.7543045Z             dim (int, optional): the dimension to operate upon. If ``None``, the
2025-03-04T20:59:08.7543596Z                 unique of the flattened input is returned. Otherwise, each of the
2025-03-04T20:59:08.7544142Z                 tensors indexed by the given dimension is treated as one of the
2025-03-04T20:59:08.7544699Z                 elements to apply the unique operation upon. See examples for more
2025-03-04T20:59:08.7545188Z                 details. Default: ``None``
2025-03-04T20:59:08.7545506Z     
2025-03-04T20:59:08.7545726Z         Returns:
2025-03-04T20:59:08.7546178Z             (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2025-03-04T20:59:08.7546690Z     
2025-03-04T20:59:08.7547023Z                 - **output** (*Tensor*): the output list of unique scalar elements.
2025-03-04T20:59:08.7547513Z                 - **inverse_indices** (*Tensor*): (optional) if
2025-03-04T20:59:08.7547980Z                   :attr:`return_inverse` is True, there will be an additional
2025-03-04T20:59:08.7548516Z                   returned tensor (same shape as input) representing the indices
2025-03-04T20:59:08.7549062Z                   for where elements in the original input map to in the output;
2025-03-04T20:59:08.7549591Z                   otherwise, this function will only return a single tensor.
2025-03-04T20:59:08.7550045Z                 - **counts** (*Tensor*): (optional) if
2025-03-04T20:59:08.7550485Z                   :attr:`return_counts` is True, there will be an additional
2025-03-04T20:59:08.7550982Z                   returned tensor (same shape as output or output.size(dim),
2025-03-04T20:59:08.7551500Z                   if dim was specified) representing the number of occurrences
2025-03-04T20:59:08.7551980Z                   for each unique value or tensor.
2025-03-04T20:59:08.7552316Z     
2025-03-04T20:59:08.7552544Z         Example::
2025-03-04T20:59:08.7552787Z     
2025-03-04T20:59:08.7553138Z             >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2025-03-04T20:59:08.7553592Z             >>> output
2025-03-04T20:59:08.7553865Z             tensor([1, 2, 3])
2025-03-04T20:59:08.7554147Z     
2025-03-04T20:59:08.7554415Z             >>> output, inverse_indices = torch.unique(
2025-03-04T20:59:08.7554917Z             ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-04T20:59:08.7555388Z             >>> output
2025-03-04T20:59:08.7555660Z             tensor([1, 2, 3])
2025-03-04T20:59:08.7555957Z             >>> inverse_indices
2025-03-04T20:59:08.7556259Z             tensor([0, 2, 1, 2])
2025-03-04T20:59:08.7556546Z     
2025-03-04T20:59:08.7556802Z             >>> output, inverse_indices = torch.unique(
2025-03-04T20:59:08.7557313Z             ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2025-03-04T20:59:08.7557813Z             >>> output
2025-03-04T20:59:08.7558085Z             tensor([1, 2, 3])
2025-03-04T20:59:08.7558385Z             >>> inverse_indices
2025-03-04T20:59:08.7558714Z             tensor([[0, 2],
2025-03-04T20:59:08.7559001Z                     [1, 2]])
2025-03-04T20:59:08.7559276Z     
2025-03-04T20:59:08.7559514Z             >>> a = torch.tensor([
2025-03-04T20:59:08.7559827Z             ...     [
2025-03-04T20:59:08.7560095Z             ...         [1, 1, 0, 0],
2025-03-04T20:59:08.7560411Z             ...         [1, 1, 0, 0],
2025-03-04T20:59:08.7560721Z             ...         [0, 0, 1, 1],
2025-03-04T20:59:08.7561023Z             ...     ],
2025-03-04T20:59:08.7561284Z             ...     [
2025-03-04T20:59:08.7561548Z             ...         [0, 0, 1, 1],
2025-03-04T20:59:08.7561848Z             ...         [0, 0, 1, 1],
2025-03-04T20:59:08.7562160Z             ...         [1, 1, 1, 1],
2025-03-04T20:59:08.7562462Z             ...     ],
2025-03-04T20:59:08.7562734Z             ...     [
2025-03-04T20:59:08.7562999Z             ...         [1, 1, 0, 0],
2025-03-04T20:59:08.7563318Z             ...         [1, 1, 0, 0],
2025-03-04T20:59:08.7563638Z             ...         [0, 0, 1, 1],
2025-03-04T20:59:08.7563942Z             ...     ],
2025-03-04T20:59:08.7564212Z             ... ])
2025-03-04T20:59:08.7564461Z     
2025-03-04T20:59:08.7564821Z             >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2025-03-04T20:59:08.7565399Z             >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2025-03-04T20:59:08.7565921Z             >>> # each other, so one of them will be removed.
2025-03-04T20:59:08.7566314Z             >>> (a[0, :, :] == a[2, :, :]).all()
2025-03-04T20:59:08.7566654Z             tensor(True)
2025-03-04T20:59:08.7566977Z             >>> a_unique_dim0 = torch.unique(a, dim=0)
2025-03-04T20:59:08.7567337Z             >>> a_unique_dim0
2025-03-04T20:59:08.7567632Z             tensor([[[0, 0, 1, 1],
2025-03-04T20:59:08.7567944Z                      [0, 0, 1, 1],
2025-03-04T20:59:08.7568251Z                      [1, 1, 1, 1]],
2025-03-04T20:59:08.7568557Z                     [[1, 1, 0, 0],
2025-03-04T20:59:08.7568857Z                      [1, 1, 0, 0],
2025-03-04T20:59:08.7569156Z                      [0, 0, 1, 1]]])
2025-03-04T20:59:08.7569451Z     
2025-03-04T20:59:08.7569806Z             >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2025-03-04T20:59:08.7570259Z             >>> # `a_unique_dim0`:
2025-03-04T20:59:08.7570602Z             >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2025-03-04T20:59:08.7570955Z             tensor(True)
2025-03-04T20:59:08.7571264Z             >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2025-03-04T20:59:08.7571614Z             tensor(True)
2025-03-04T20:59:08.7571913Z     
2025-03-04T20:59:08.7572258Z             >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2025-03-04T20:59:08.7572802Z             >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2025-03-04T20:59:08.7573241Z             >>> # them will be removed.
2025-03-04T20:59:08.7573743Z             >>> (a[:, 0, :] == a[:, 1, :]).all()
2025-03-04T20:59:08.7574142Z             tensor(True)
2025-03-04T20:59:08.7574443Z             >>> torch.unique(a, dim=1)
2025-03-04T20:59:08.7574778Z             tensor([[[0, 0, 1, 1],
2025-03-04T20:59:08.7575089Z                      [1, 1, 0, 0]],
2025-03-04T20:59:08.7575392Z                     [[1, 1, 1, 1],
2025-03-04T20:59:08.7575693Z                      [0, 0, 1, 1]],
2025-03-04T20:59:08.7575992Z                     [[0, 0, 1, 1],
2025-03-04T20:59:08.7576290Z                      [1, 1, 0, 0]]])
2025-03-04T20:59:08.7576583Z     
2025-03-04T20:59:08.7576929Z             >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2025-03-04T20:59:08.7577444Z             >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2025-03-04T20:59:08.7578092Z             >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2025-03-04T20:59:08.7578536Z             >>> # sub-tensors will be removed.
2025-03-04T20:59:08.7578948Z             >>> (a[:, :, 0] == a[:, :, 1]).all()
2025-03-04T20:59:08.7579283Z             tensor(True)
2025-03-04T20:59:08.7579579Z             >>> (a[:, :, 2] == a[:, :, 3]).all()
2025-03-04T20:59:08.7579916Z             tensor(True)
2025-03-04T20:59:08.7580213Z             >>> torch.unique(a, dim=2)
2025-03-04T20:59:08.7580533Z             tensor([[[0, 1],
2025-03-04T20:59:08.7580827Z                      [0, 1],
2025-03-04T20:59:08.7581111Z                      [1, 0]],
2025-03-04T20:59:08.7581396Z                     [[1, 0],
2025-03-04T20:59:08.7581676Z                      [1, 0],
2025-03-04T20:59:08.7581956Z                      [1, 1]],
2025-03-04T20:59:08.7582241Z                     [[0, 1],
2025-03-04T20:59:08.7582518Z                      [0, 1],
2025-03-04T20:59:08.7582802Z                      [1, 0]]])
2025-03-04T20:59:08.7583081Z         
2025-03-04T20:59:08.7583481Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7583958Z     
2025-03-04T20:59:08.7584194Z       warnings.warn(msg)
2025-03-04T20:59:08.7584465Z     
2025-03-04T20:59:08.7584835Z --- Parse Warning: 4 / 116 ---
2025-03-04T20:59:08.7585882Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560.
2025-03-04T20:59:08.7587132Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7587705Z     
2025-03-04T20:59:08.7588005Z     Load a model from a github repo or a local directory.
2025-03-04T20:59:08.7588386Z     
2025-03-04T20:59:08.7588754Z     Note: Loading a model is the typical use case, but this can also be used to
2025-03-04T20:59:08.7589352Z     for loading other objects such as tokenizers, loss functions, etc.
2025-03-04T20:59:08.7589828Z     
2025-03-04T20:59:08.7590133Z     If ``source`` is 'github', ``repo_or_dir`` is expected to be
2025-03-04T20:59:08.7590622Z     of the form ``repo_owner/repo_name[:ref]`` with an optional
2025-03-04T20:59:08.7591034Z     ref (a tag or a branch).
2025-03-04T20:59:08.7591324Z     
2025-03-04T20:59:08.7591636Z     If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2025-03-04T20:59:08.7592051Z     path to a local directory.
2025-03-04T20:59:08.7592346Z     
2025-03-04T20:59:08.7592563Z     Args:
2025-03-04T20:59:08.7592845Z         repo_or_dir (str): If ``source`` is 'github',
2025-03-04T20:59:08.7593408Z             this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2025-03-04T20:59:08.7594142Z             an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2025-03-04T20:59:08.7594920Z             the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2025-03-04T20:59:08.7595538Z             If ``source`` is 'local'  then it should be a path to a local directory.
2025-03-04T20:59:08.7596090Z         model (str): the name of a callable (entrypoint) defined in the
2025-03-04T20:59:08.7596529Z             repo/dir's ``hubconf.py``.
2025-03-04T20:59:08.7596970Z         *args (optional): the corresponding args for callable ``model``.
2025-03-04T20:59:08.7597497Z         source (str, optional): 'github' or 'local'. Specifies how
2025-03-04T20:59:08.7598083Z             ``repo_or_dir`` is to be interpreted. Default is 'github'.
2025-03-04T20:59:08.7598703Z         trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2025-03-04T20:59:08.7599302Z             This parameter was introduced in v1.12 and helps ensuring that users
2025-03-04T20:59:08.7599809Z             only run code from repos that they trust.
2025-03-04T20:59:08.7600163Z     
2025-03-04T20:59:08.7600543Z             - If ``False``, a prompt will ask the user whether the repo should
2025-03-04T20:59:08.7600981Z               be trusted.
2025-03-04T20:59:08.7601371Z             - If ``True``, the repo will be added to the trusted list and loaded
2025-03-04T20:59:08.7601922Z               without requiring explicit confirmation.
2025-03-04T20:59:08.7602378Z             - If ``"check"``, the repo will be checked against the list of
2025-03-04T20:59:08.7602914Z               trusted repos in the cache. If it is not present in that list, the
2025-03-04T20:59:08.7603469Z               behaviour will fall back onto the ``trust_repo=False`` option.
2025-03-04T20:59:08.7603994Z             - If ``None``: this will raise a warning, inviting the user to set
2025-03-04T20:59:08.7604506Z               ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2025-03-04T20:59:08.7605049Z               is only present for backward compatibility and will be removed in
2025-03-04T20:59:08.7605487Z               v2.0.
2025-03-04T20:59:08.7605733Z     
2025-03-04T20:59:08.7606123Z             Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2025-03-04T20:59:08.7606683Z         force_reload (bool, optional): whether to force a fresh download of
2025-03-04T20:59:08.7607231Z             the github repo unconditionally. Does not have any effect if
2025-03-04T20:59:08.7607689Z             ``source = 'local'``. Default is ``False``.
2025-03-04T20:59:08.7608145Z         verbose (bool, optional): If ``False``, mute messages about hitting
2025-03-04T20:59:08.7608703Z             local caches. Note that the message about first download cannot be
2025-03-04T20:59:08.7609255Z             muted. Does not have any effect if ``source = 'local'``.
2025-03-04T20:59:08.7609665Z             Default is ``True``.
2025-03-04T20:59:08.7610179Z         skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2025-03-04T20:59:08.7610909Z             specified by the ``github`` argument properly belongs to the repo owner. This will make
2025-03-04T20:59:08.7611614Z             requests to the GitHub API; you can specify a non-default GitHub token by setting the
2025-03-04T20:59:08.7612213Z             ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2025-03-04T20:59:08.7612753Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-04T20:59:08.7613196Z     
2025-03-04T20:59:08.7613418Z     Returns:
2025-03-04T20:59:08.7613766Z         The output of the ``model`` callable when called with the given
2025-03-04T20:59:08.7614192Z         ``*args`` and ``**kwargs``.
2025-03-04T20:59:08.7614497Z     
2025-03-04T20:59:08.7614721Z     Example:
2025-03-04T20:59:08.7615015Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-04T20:59:08.7615388Z         >>> # from a github repo
2025-03-04T20:59:08.7615707Z         >>> repo = "pytorch/vision"
2025-03-04T20:59:08.7616071Z         >>> model = torch.hub.load(
2025-03-04T20:59:08.7616486Z         ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2025-03-04T20:59:08.7616886Z         ... )
2025-03-04T20:59:08.7617144Z         >>> # from a local directory
2025-03-04T20:59:08.7617511Z         >>> path = "/some/local/path/pytorch/vision"
2025-03-04T20:59:08.7617985Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.7618443Z         >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2025-03-04T20:59:08.7618920Z     
2025-03-04T20:59:08.7619317Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7619796Z     
2025-03-04T20:59:08.7620034Z       warnings.warn(msg)
2025-03-04T20:59:08.7620308Z     
2025-03-04T20:59:08.7620668Z --- Parse Warning: 5 / 116 ---
2025-03-04T20:59:08.7621748Z     /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=652.
2025-03-04T20:59:08.7622996Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7623487Z     
2025-03-04T20:59:08.7623803Z     Load a model from a local directory with a ``hubconf.py``.
2025-03-04T20:59:08.7624234Z     
2025-03-04T20:59:08.7624457Z     Args:
2025-03-04T20:59:08.7624788Z         hubconf_dir (str): path to a local directory that contains a
2025-03-04T20:59:08.7625215Z             ``hubconf.py``.
2025-03-04T20:59:08.7625589Z         model (str): name of an entrypoint defined in the directory's
2025-03-04T20:59:08.7626007Z             ``hubconf.py``.
2025-03-04T20:59:08.7626412Z         *args (optional): the corresponding args for callable ``model``.
2025-03-04T20:59:08.7626965Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-03-04T20:59:08.7627404Z     
2025-03-04T20:59:08.7627625Z     Returns:
2025-03-04T20:59:08.7627950Z         a single model with corresponding pretrained weights.
2025-03-04T20:59:08.7628337Z     
2025-03-04T20:59:08.7628561Z     Example:
2025-03-04T20:59:08.7628834Z         >>> # xdoctest: +SKIP("stub local path")
2025-03-04T20:59:08.7629227Z         >>> path = "/some/local/path/pytorch/vision"
2025-03-04T20:59:08.7629599Z         >>> model = _load_local(
2025-03-04T20:59:08.7629903Z         ...     path,
2025-03-04T20:59:08.7630173Z         ...     "resnet50",
2025-03-04T20:59:08.7630516Z         ...     weights="ResNet50_Weights.IMAGENET1K_V1",
2025-03-04T20:59:08.7630878Z         ... )
2025-03-04T20:59:08.7631100Z     
2025-03-04T20:59:08.7631493Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7632022Z     
2025-03-04T20:59:08.7632257Z       warnings.warn(msg)
2025-03-04T20:59:08.7632536Z     
2025-03-04T20:59:08.7632871Z --- Parse Warning: 6 / 116 ---
2025-03-04T20:59:08.7634007Z     /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=691.
2025-03-04T20:59:08.7635277Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7635843Z     Download object at the given URL to a local path.
2025-03-04T20:59:08.7636216Z     
2025-03-04T20:59:08.7636438Z         Args:
2025-03-04T20:59:08.7636726Z             url (str): URL of the object to download
2025-03-04T20:59:08.7637230Z             dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2025-03-04T20:59:08.7637948Z             hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2025-03-04T20:59:08.7638524Z                 Default: None
2025-03-04T20:59:08.7638989Z             progress (bool, optional): whether or not to display a progress bar to stderr
2025-03-04T20:59:08.7639518Z                 Default: True
2025-03-04T20:59:08.7639809Z     
2025-03-04T20:59:08.7640039Z         Example:
2025-03-04T20:59:08.7640347Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-04T20:59:08.7640744Z             >>> # xdoctest: +REQUIRES(POSIX)
2025-03-04T20:59:08.7641101Z             >>> torch.hub.download_url_to_file(
2025-03-04T20:59:08.7641598Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2025-03-04T20:59:08.7642074Z             ...     "/tmp/temporary_file",
2025-03-04T20:59:08.7642400Z             ... )
2025-03-04T20:59:08.7642644Z     
2025-03-04T20:59:08.7642862Z         
2025-03-04T20:59:08.7643259Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7643732Z     
2025-03-04T20:59:08.7643964Z       warnings.warn(msg)
2025-03-04T20:59:08.7644235Z     
2025-03-04T20:59:08.7644557Z --- Parse Warning: 7 / 116 ---
2025-03-04T20:59:08.7645681Z     /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=816.
2025-03-04T20:59:08.7647082Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7647677Z     Loads the Torch serialized object at the given URL.
2025-03-04T20:59:08.7648051Z     
2025-03-04T20:59:08.7648372Z         If downloaded file is a zip file, it will be automatically
2025-03-04T20:59:08.7648790Z         decompressed.
2025-03-04T20:59:08.7649056Z     
2025-03-04T20:59:08.7649412Z         If the object is already present in `model_dir`, it's deserialized and
2025-03-04T20:59:08.7649861Z         returned.
2025-03-04T20:59:08.7650248Z         The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2025-03-04T20:59:08.7650805Z         ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2025-03-04T20:59:08.7651212Z     
2025-03-04T20:59:08.7651435Z         Args:
2025-03-04T20:59:08.7651715Z             url (str): URL of the object to download
2025-03-04T20:59:08.7652181Z             model_dir (str, optional): directory in which to save the object
2025-03-04T20:59:08.7652870Z             map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2025-03-04T20:59:08.7653626Z             progress (bool, optional): whether or not to display a progress bar to stderr.
2025-03-04T20:59:08.7654120Z                 Default: True
2025-03-04T20:59:08.7654649Z             check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2025-03-04T20:59:08.7655373Z                 ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2025-03-04T20:59:08.7655972Z                 digits of the SHA256 hash of the contents of the file. The hash is used to
2025-03-04T20:59:08.7656536Z                 ensure unique names and to verify the contents of the file.
2025-03-04T20:59:08.7656969Z                 Default: False
2025-03-04T20:59:08.7657518Z             file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2025-03-04T20:59:08.7658401Z             weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2025-03-04T20:59:08.7659134Z                 Recommended for untrusted sources. See :func:`~torch.load` for more details.
2025-03-04T20:59:08.7659626Z     
2025-03-04T20:59:08.7659849Z         Example:
2025-03-04T20:59:08.7660147Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-03-04T20:59:08.7660574Z             >>> state_dict = torch.hub.load_state_dict_from_url(
2025-03-04T20:59:08.7661084Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2025-03-04T20:59:08.7661527Z             ... )
2025-03-04T20:59:08.7661770Z     
2025-03-04T20:59:08.7661988Z         
2025-03-04T20:59:08.7662371Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7662884Z     
2025-03-04T20:59:08.7663116Z       warnings.warn(msg)
2025-03-04T20:59:08.7663393Z     
2025-03-04T20:59:08.7663730Z --- Parse Warning: 8 / 116 ---
2025-03-04T20:59:08.7664860Z     /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=376.
2025-03-04T20:59:08.7666106Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:59:08.7666742Z     Registers the function implementation as the fallback for the given key.
2025-03-04T20:59:08.7667207Z     
2025-03-04T20:59:08.7667567Z             This function only works for a library with global namespace ("_").
2025-03-04T20:59:08.7668009Z     
2025-03-04T20:59:08.7668227Z             Args:
2025-03-04T20:59:08.7668674Z                 fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2025-03-04T20:59:08.7669220Z                     to register a fallthrough.
2025-03-04T20:59:08.7669834Z                 dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2025-03-04T20:59:08.7670476Z                               the dispatch key that the library was created with.
2025-03-04T20:59:08.7671192Z                 with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2025-03-04T20:59:08.7672045Z                              to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2025-03-04T20:59:08.7672596Z     
2025-03-04T20:59:08.7672841Z             Example::
2025-03-04T20:59:08.7673144Z                 >>> my_lib = Library("_", "IMPL")
2025-03-04T20:59:08.7673531Z                 >>> def fallback_kernel(op, *args, **kwargs):
2025-03-04T20:59:08.7674432Z                 >>>     # Handle all autocast ops generically
2025-03-04T20:59:08.7675019Z                 >>>     # ...
2025-03-04T20:59:08.7675569Z                 >>> my_lib.fallback(fallback_kernel, "Autocast")
2025-03-04T20:59:08.7676180Z             
2025-03-04T20:59:08.7677011Z     Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2025-03-04T20:59:08.7677862Z     
2025-03-04T20:59:08.7678137Z     my_lib.fallback(fallback_kernel, "Autocast")
2025-03-04T20:59:08.7678498Z     ^
2025-03-04T20:59:08.7678740Z       warnings.warn(msg)
2025-03-04T20:59:08.7679021Z     
2025-03-04T20:59:08.7679382Z --- Parse Warning: 9 / 116 ---
2025-03-04T20:59:08.7680603Z     /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=920.
2025-03-04T20:59:08.7681842Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:59:08.7682475Z     Register a FakeTensor implementation ("fake impl") for this operator.
2025-03-04T20:59:08.7682937Z     
2025-03-04T20:59:08.7683261Z         Also sometimes known as a "meta kernel", "abstract impl".
2025-03-04T20:59:08.7683667Z     
2025-03-04T20:59:08.7684061Z         An "FakeTensor implementation" specifies the behavior of this operator on
2025-03-04T20:59:08.7684679Z         Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2025-03-04T20:59:08.7685293Z         certain properties (sizes/strides/storage_offset/device), it specifies
2025-03-04T20:59:08.7685827Z         what the properties of the output Tensors are.
2025-03-04T20:59:08.7686193Z     
2025-03-04T20:59:08.7686565Z         The FakeTensor implementation has the same signature as the operator.
2025-03-04T20:59:08.7687153Z         It is run for both FakeTensors and meta tensors. To write a FakeTensor
2025-03-04T20:59:08.7687761Z         implementation, assume that all Tensor inputs to the operator are
2025-03-04T20:59:08.7688328Z         regular CPU/CUDA/Meta tensors, but they do not have storage, and
2025-03-04T20:59:08.7688884Z         you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2025-03-04T20:59:08.7689462Z         The FakeTensor implementation must consist of only PyTorch operations
2025-03-04T20:59:08.7690030Z         (and may not directly access the storage or data of any input or
2025-03-04T20:59:08.7690563Z         intermediate Tensors).
2025-03-04T20:59:08.7690857Z     
2025-03-04T20:59:08.7691149Z         This API may be used as a decorator (see examples).
2025-03-04T20:59:08.7691524Z     
2025-03-04T20:59:08.7691803Z         For a detailed guide on custom ops, please see
2025-03-04T20:59:08.7692321Z         https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2025-03-04T20:59:08.7692781Z     
2025-03-04T20:59:08.7693005Z         Examples:
2025-03-04T20:59:08.7693273Z             >>> import torch
2025-03-04T20:59:08.7693582Z             >>> import numpy as np
2025-03-04T20:59:08.7693919Z             >>> from torch import Tensor
2025-03-04T20:59:08.7694304Z             >>>
2025-03-04T20:59:08.7694659Z             >>> # Example 1: an operator without data-dependent output shape
2025-03-04T20:59:08.7695264Z             >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2025-03-04T20:59:08.7695847Z             >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-03-04T20:59:08.7696382Z             >>>     raise NotImplementedError("Implementation goes here")
2025-03-04T20:59:08.7696792Z             >>>
2025-03-04T20:59:08.7697129Z             >>> @torch.library.register_fake("mylib::custom_linear")
2025-03-04T20:59:08.7697545Z             >>> def _(x, weight, bias):
2025-03-04T20:59:08.7697965Z             >>>     assert x.dim() == 2
2025-03-04T20:59:08.7698319Z             >>>     assert weight.dim() == 2
2025-03-04T20:59:08.7698680Z             >>>     assert bias.dim() == 1
2025-03-04T20:59:08.7699054Z             >>>     assert x.shape[1] == weight.shape[1]
2025-03-04T20:59:08.7699462Z             >>>     assert weight.shape[0] == bias.shape[0]
2025-03-04T20:59:08.7699864Z             >>>     assert x.device == weight.device
2025-03-04T20:59:08.7700210Z             >>>
2025-03-04T20:59:08.7700485Z             >>>     return (x @ weight.t()) + bias
2025-03-04T20:59:08.7700822Z             >>>
2025-03-04T20:59:08.7701159Z             >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2025-03-04T20:59:08.7701579Z             >>>     x = torch.randn(2, 3)
2025-03-04T20:59:08.7701921Z             >>>     w = torch.randn(3, 3)
2025-03-04T20:59:08.7702259Z             >>>     b = torch.randn(3)
2025-03-04T20:59:08.7702663Z             >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2025-03-04T20:59:08.7703031Z             >>>
2025-03-04T20:59:08.7703284Z             >>> assert y.shape == (2, 3)
2025-03-04T20:59:08.7703607Z             >>>
2025-03-04T20:59:08.7703943Z             >>> # Example 2: an operator with data-dependent output shape
2025-03-04T20:59:08.7704485Z             >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2025-03-04T20:59:08.7704979Z             >>> def custom_nonzero(x: Tensor) -> Tensor:
2025-03-04T20:59:08.7705359Z             >>>     x_np = x.numpy(force=True)
2025-03-04T20:59:08.7705741Z             >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-03-04T20:59:08.7706149Z             >>>     return torch.tensor(res, device=x.device)
2025-03-04T20:59:08.7706507Z             >>>
2025-03-04T20:59:08.7706839Z             >>> @torch.library.register_fake("mylib::custom_nonzero")
2025-03-04T20:59:08.7707239Z             >>> def _(x):
2025-03-04T20:59:08.7707580Z             >>> # Number of nonzero-elements is data-dependent.
2025-03-04T20:59:08.7708025Z             >>> # Since we cannot peek at the data in an fake impl,
2025-03-04T20:59:08.7708475Z             >>> # we use the ctx object to construct a new symint that
2025-03-04T20:59:08.7708939Z             >>> # represents the data-dependent size.
2025-03-04T20:59:08.7709326Z             >>>     ctx = torch.library.get_ctx()
2025-03-04T20:59:08.7709714Z             >>>     nnz = ctx.new_dynamic_size()
2025-03-04T20:59:08.7710081Z             >>>     shape = [nnz, x.dim()]
2025-03-04T20:59:08.7710479Z             >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-03-04T20:59:08.7710869Z             >>>     return result
2025-03-04T20:59:08.7711158Z             >>>
2025-03-04T20:59:08.7711499Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-04T20:59:08.7711903Z             >>>
2025-03-04T20:59:08.7712181Z             >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2025-03-04T20:59:08.7712677Z             >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2025-03-04T20:59:08.7713183Z             >>> trace.print_readable()
2025-03-04T20:59:08.7713505Z             >>>
2025-03-04T20:59:08.7713892Z             >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2025-03-04T20:59:08.7714351Z     
2025-03-04T20:59:08.7714606Z         
2025-03-04T20:59:08.7715283Z     Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2025-03-04T20:59:08.7716039Z     
2025-03-04T20:59:08.7716298Z     _._ = None
2025-03-04T20:59:08.7716544Z     ^
2025-03-04T20:59:08.7716785Z       warnings.warn(msg)
2025-03-04T20:59:08.7717068Z     
2025-03-04T20:59:08.7717466Z --- Parse Warning: 10 / 116 ---
2025-03-04T20:59:08.7718616Z     /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=1041.
2025-03-04T20:59:08.7719885Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7720443Z     Register a backward formula for this custom op.
2025-03-04T20:59:08.7720811Z     
2025-03-04T20:59:08.7721165Z         In order for an operator to work with autograd, you need to register
2025-03-04T20:59:08.7721622Z         a backward formula:
2025-03-04T20:59:08.7722033Z         1. You must tell us how to compute gradients during the backward pass
2025-03-04T20:59:08.7722511Z         by providing us a "backward" function.
2025-03-04T20:59:08.7722986Z         2. If you need any values from the forward to compute gradients, you can
2025-03-04T20:59:08.7723490Z         use `setup_context` to save values for backward.
2025-03-04T20:59:08.7723852Z     
2025-03-04T20:59:08.7724245Z         ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2025-03-04T20:59:08.7724840Z         - ``grads`` is one or more gradients. The number of gradients matches
2025-03-04T20:59:08.7725306Z         the number of outputs of the operator.
2025-03-04T20:59:08.7725781Z         The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2025-03-04T20:59:08.7726389Z         :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2025-03-04T20:59:08.7726935Z         same as :meth:`torch.autograd.Function.backward`.
2025-03-04T20:59:08.7727318Z     
2025-03-04T20:59:08.7727669Z         ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2025-03-04T20:59:08.7728259Z         Please save quantities needed for backward onto the ``ctx`` object via
2025-03-04T20:59:08.7728868Z         either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2025-03-04T20:59:08.7729449Z         or assigning them as attributes of ``ctx``. If your custom op has
2025-03-04T20:59:08.7730008Z         kwarg-only arguments, we expect the signature of ``setup_context``
2025-03-04T20:59:08.7730561Z         to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2025-03-04T20:59:08.7730980Z     
2025-03-04T20:59:08.7731341Z         Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2025-03-04T20:59:08.7731948Z         they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2025-03-04T20:59:08.7732554Z         not depend on or mutate global state. If you need a non-traceable backward,
2025-03-04T20:59:08.7733157Z         you can make it a separate custom_op that you call inside ``backward_fn``.
2025-03-04T20:59:08.7733606Z     
2025-03-04T20:59:08.7733959Z         If you need different autograd behavior on different devices, then we
2025-03-04T20:59:08.7734552Z         recommend creating two different custom operators, one for each device
2025-03-04T20:59:08.7735152Z         that needs different behavior, and switching between them at runtime.
2025-03-04T20:59:08.7735598Z     
2025-03-04T20:59:08.7735820Z         Examples:
2025-03-04T20:59:08.7736086Z             >>> import torch
2025-03-04T20:59:08.7736393Z             >>> import numpy as np
2025-03-04T20:59:08.7736724Z             >>> from torch import Tensor
2025-03-04T20:59:08.7737085Z             >>>
2025-03-04T20:59:08.7737459Z             >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2025-03-04T20:59:08.7738037Z             >>> def numpy_sin(x: Tensor) -> Tensor:
2025-03-04T20:59:08.7738408Z             >>>     x_np = x.cpu().numpy()
2025-03-04T20:59:08.7738758Z             >>>     y_np = np.sin(x_np)
2025-03-04T20:59:08.7739191Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-04T20:59:08.7739574Z             >>>
2025-03-04T20:59:08.7739883Z             >>> def setup_context(ctx, inputs, output) -> Tensor:
2025-03-04T20:59:08.7740257Z             >>>     x, = inputs
2025-03-04T20:59:08.7740575Z             >>>     ctx.save_for_backward(x)
2025-03-04T20:59:08.7740902Z             >>>
2025-03-04T20:59:08.7741167Z             >>> def backward(ctx, grad):
2025-03-04T20:59:08.7741513Z             >>>     x, = ctx.saved_tensors
2025-03-04T20:59:08.7741851Z             >>>     return grad * x.cos()
2025-03-04T20:59:08.7742167Z             >>>
2025-03-04T20:59:08.7742449Z             >>> torch.library.register_autograd(
2025-03-04T20:59:08.7742885Z             ...     "mylib::numpy_sin", backward, setup_context=setup_context
2025-03-04T20:59:08.7743293Z             ... )
2025-03-04T20:59:08.7743536Z             >>>
2025-03-04T20:59:08.7743817Z             >>> x = torch.randn(3, requires_grad=True)
2025-03-04T20:59:08.7744177Z             >>> y = numpy_sin(x)
2025-03-04T20:59:08.7744561Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-04T20:59:08.7745006Z             >>> assert torch.allclose(grad_x, x.cos())
2025-03-04T20:59:08.7745352Z             >>>
2025-03-04T20:59:08.7745630Z             >>> # Example with a keyword-only arg
2025-03-04T20:59:08.7746123Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-04T20:59:08.7746611Z             >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2025-03-04T20:59:08.7747009Z             >>>     x_np = x.cpu().numpy()
2025-03-04T20:59:08.7747355Z             >>>     y_np = x_np * val
2025-03-04T20:59:08.7747743Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-03-04T20:59:08.7748137Z             >>>
2025-03-04T20:59:08.7748525Z             >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2025-03-04T20:59:08.7749026Z             >>>     ctx.val = keyword_only_inputs["val"]
2025-03-04T20:59:08.7749377Z             >>>
2025-03-04T20:59:08.7749639Z             >>> def backward(ctx, grad):
2025-03-04T20:59:08.7774508Z             >>>     return grad * ctx.val
2025-03-04T20:59:08.7774826Z             >>>
2025-03-04T20:59:08.7775100Z             >>> torch.library.register_autograd(
2025-03-04T20:59:08.7775539Z             ...     "mylib::numpy_mul", backward, setup_context=setup_context
2025-03-04T20:59:08.7775939Z             ... )
2025-03-04T20:59:08.7776163Z             >>>
2025-03-04T20:59:08.7776429Z             >>> x = torch.randn(3, requires_grad=True)
2025-03-04T20:59:08.7776777Z             >>> y = numpy_mul(x, val=3.14)
2025-03-04T20:59:08.7777323Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-03-04T20:59:08.7777861Z             >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2025-03-04T20:59:08.7778239Z     
2025-03-04T20:59:08.7778435Z         
2025-03-04T20:59:08.7778813Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7779289Z     
2025-03-04T20:59:08.7779498Z       warnings.warn(msg)
2025-03-04T20:59:08.7779741Z     
2025-03-04T20:59:08.7780110Z --- Parse Warning: 11 / 116 ---
2025-03-04T20:59:08.7781198Z     /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=1455.
2025-03-04T20:59:08.7782406Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7783006Z     Given an operator and some sample arguments, tests if the operator is
2025-03-04T20:59:08.7783449Z         registered correctly.
2025-03-04T20:59:08.7783717Z     
2025-03-04T20:59:08.7784109Z         That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2025-03-04T20:59:08.7784697Z         custom op, you specified metadata (e.g. mutability info) about the custom op
2025-03-04T20:59:08.7785366Z         and these APIs require that the functions you pass them satisfy certain
2025-03-04T20:59:08.7785943Z         properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2025-03-04T20:59:08.7786442Z         ``opcheck`` tests these metadata and properties.
2025-03-04T20:59:08.7786788Z     
2025-03-04T20:59:08.7787017Z         Concretely, we test the following:
2025-03-04T20:59:08.7787326Z     
2025-03-04T20:59:08.7787617Z         - test_schema: If the schema matches the implementation of
2025-03-04T20:59:08.7788137Z           the operator. For example: if the schema specifies a Tensor is mutated,
2025-03-04T20:59:08.7788691Z           then we check the implementation mutates the Tensor. If the schema
2025-03-04T20:59:08.7789215Z           specifies that we return a new Tensor, then we check that the
2025-03-04T20:59:08.7789761Z           implementation returns a new Tensor (instead of an existing one or
2025-03-04T20:59:08.7790206Z           a view of an existing one).
2025-03-04T20:59:08.7790618Z         - test_autograd_registration: If the operator supports training
2025-03-04T20:59:08.7791136Z           (autograd): we check that its autograd formula is registered via
2025-03-04T20:59:08.7791662Z           torch.library.register_autograd or a manual registration to one
2025-03-04T20:59:08.7792209Z           or more DispatchKey::Autograd keys. Any other DispatchKey-based
2025-03-04T20:59:08.7793196Z           registrations may lead to undefined behavior.
2025-03-04T20:59:08.7793658Z         - test_faketensor: If the operator has a FakeTensor kernel
2025-03-04T20:59:08.7794181Z           (and if it is correct). The FakeTensor kernel is necessary (
2025-03-04T20:59:08.7794725Z           but not sufficient) for the operator to work with PyTorch compilation
2025-03-04T20:59:08.7795287Z           APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2025-03-04T20:59:08.7795816Z           (also sometimes known as a meta kernel) was registered for the
2025-03-04T20:59:08.7796325Z           operator and that it is correct. This test takes the result of
2025-03-04T20:59:08.7796834Z           running the operator on real tensors and the result of running
2025-03-04T20:59:08.7797356Z           the operator on FakeTensors and checks that they have the same
2025-03-04T20:59:08.7797829Z           Tensor metadata (sizes/strides/dtype/device/etc).
2025-03-04T20:59:08.7798306Z         - test_aot_dispatch_dynamic: If the operator has correct behavior
2025-03-04T20:59:08.7798822Z           with PyTorch compilation APIs (torch.compile/export/FX).
2025-03-04T20:59:08.7799354Z           This checks that the outputs (and gradients, if applicable) are the
2025-03-04T20:59:08.7799892Z           same under eager-mode PyTorch and torch.compile.
2025-03-04T20:59:08.7800377Z           This test is a superset of ``test_faketensor`` and is an e2e test;
2025-03-04T20:59:08.7800867Z           other things it tests are that the operator supports
2025-03-04T20:59:08.7801373Z           functionalization and that the backward pass (if it exists) also
2025-03-04T20:59:08.7801863Z           supports FakeTensor and functionalization.
2025-03-04T20:59:08.7802203Z     
2025-03-04T20:59:08.7802516Z         For best results, please call ``opcheck`` multiple times with a
2025-03-04T20:59:08.7803017Z         representative set of inputs. If your operator supports
2025-03-04T20:59:08.7803573Z         autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2025-03-04T20:59:08.7804151Z         if your operator supports multiple devices (e.g. CPU and CUDA), please
2025-03-04T20:59:08.7804667Z         use ``opcheck`` with inputs on all supported devices.
2025-03-04T20:59:08.7805024Z     
2025-03-04T20:59:08.7805217Z         Args:
2025-03-04T20:59:08.7805536Z             op: The operator. Must either be a function decorated with
2025-03-04T20:59:08.7806090Z                 :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2025-03-04T20:59:08.7806688Z                 found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2025-03-04T20:59:08.7807147Z             args: The args to the operator
2025-03-04T20:59:08.7807494Z             kwargs: The kwargs to the operator
2025-03-04T20:59:08.7807915Z             test_utils: Tests that we should run. Default: all of them.
2025-03-04T20:59:08.7808356Z                 Example: ("test_schema", "test_faketensor")
2025-03-04T20:59:08.7808837Z             raise_exception: If we should raise an exception on the first
2025-03-04T20:59:08.7809400Z                 error. If False, we will return a dict with information
2025-03-04T20:59:08.7809814Z                 on if each test passed or not.
2025-03-04T20:59:08.7810292Z             rtol (Optional[float]): Relative tolerance for floating point comparisons.
2025-03-04T20:59:08.7810807Z                 If specified ``atol`` must also be specified.
2025-03-04T20:59:08.7811283Z                 If omitted, default values based on the ``dtype`` are selected
2025-03-04T20:59:08.7811788Z                 (see the table in :func:`torch.testing.assert_close`).
2025-03-04T20:59:08.7812337Z             atol (Optional[float]): Absolute tolerance for floating point comparisons.
2025-03-04T20:59:08.7812859Z                 If specified ``rtol`` must also be specified.
2025-03-04T20:59:08.7813331Z                 If omitted, default values based on the ``dtype`` are selected
2025-03-04T20:59:08.7813871Z                 (see the table in :func:`torch.testing.assert_close`).
2025-03-04T20:59:08.7814253Z     
2025-03-04T20:59:08.7814470Z         .. warning::
2025-03-04T20:59:08.7814702Z     
2025-03-04T20:59:08.7815031Z             opcheck and :func:`torch.autograd.gradcheck` test different things;
2025-03-04T20:59:08.7815672Z             opcheck tests if your usage of torch.library APIs is correct while
2025-03-04T20:59:08.7816226Z             :func:`torch.autograd.gradcheck` tests if your autograd formula is
2025-03-04T20:59:08.7816781Z             mathematically correct. Use both to test custom ops that support
2025-03-04T20:59:08.7817237Z             gradient computation.
2025-03-04T20:59:08.7817334Z     
2025-03-04T20:59:08.7817425Z         Example:
2025-03-04T20:59:08.7817513Z     
2025-03-04T20:59:08.7817663Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:08.7817965Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-03-04T20:59:08.7818138Z             >>> def numpy_mul(x: Tensor, y: float) -> Tensor:
2025-03-04T20:59:08.7818276Z             >>>     x_np = x.numpy(force=True)
2025-03-04T20:59:08.7818387Z             >>>     z_np = x_np * y
2025-03-04T20:59:08.7818550Z             >>>     return torch.from_numpy(z_np).to(x.device)
2025-03-04T20:59:08.7818680Z             >>>
2025-03-04T20:59:08.7818808Z             >>> @numpy_mul.register_fake
2025-03-04T20:59:08.7818916Z             >>> def _(x, y):
2025-03-04T20:59:08.7819053Z             >>>     return torch.empty_like(x)
2025-03-04T20:59:08.7819144Z             >>>
2025-03-04T20:59:08.7819300Z             >>> def setup_context(ctx, inputs, output):
2025-03-04T20:59:08.7819405Z             >>>     y, = inputs
2025-03-04T20:59:08.7819550Z             >>>     ctx.y = y
2025-03-04T20:59:08.7819671Z             >>>
2025-03-04T20:59:08.7819848Z             >>> def backward(ctx, grad):
2025-03-04T20:59:08.7819992Z             >>>     return grad * ctx.y, None
2025-03-04T20:59:08.7820124Z             >>>
2025-03-04T20:59:08.7820364Z             >>> numpy_mul.register_autograd(backward, setup_context=setup_context)
2025-03-04T20:59:08.7820466Z             >>>
2025-03-04T20:59:08.7820573Z             >>> sample_inputs = [
2025-03-04T20:59:08.7820701Z             >>>     (torch.randn(3), 3.14),
2025-03-04T20:59:08.7820842Z             >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2025-03-04T20:59:08.7821049Z             >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2025-03-04T20:59:08.7821247Z             >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2025-03-04T20:59:08.7821380Z             >>> ]
2025-03-04T20:59:08.7821471Z             >>>
2025-03-04T20:59:08.7821590Z             >>> for args in sample_inputs:
2025-03-04T20:59:08.7821748Z             >>>     torch.library.opcheck(numpy_mul, args)
2025-03-04T20:59:08.7821836Z     
2025-03-04T20:59:08.7821939Z         
2025-03-04T20:59:08.7822205Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7822306Z     
2025-03-04T20:59:08.7822412Z       warnings.warn(msg)
2025-03-04T20:59:08.7822512Z     
2025-03-04T20:59:08.7822761Z --- Parse Warning: 12 / 116 ---
2025-03-04T20:59:08.7823631Z     /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=1247.
2025-03-04T20:59:08.7823908Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7824264Z     load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args)
2025-03-04T20:59:08.7824355Z     
2025-03-04T20:59:08.7824557Z         Loads an object saved with :func:`torch.save` from a file.
2025-03-04T20:59:08.7824648Z     
2025-03-04T20:59:08.7824900Z         :func:`torch.load` uses Python's unpickling facilities but treats storages,
2025-03-04T20:59:08.7825167Z         which underlie tensors, specially. They are first deserialized on the
2025-03-04T20:59:08.7825421Z         CPU and are then moved to the device they were saved from. If this fails
2025-03-04T20:59:08.7825738Z         (e.g. because the run time system doesn't have certain devices), an exception
2025-03-04T20:59:08.7826006Z         is raised. However, storages can be dynamically remapped to an alternative
2025-03-04T20:59:08.7826186Z         set of devices using the :attr:`map_location` argument.
2025-03-04T20:59:08.7826287Z     
2025-03-04T20:59:08.7826540Z         If :attr:`map_location` is a callable, it will be called once for each serialized
2025-03-04T20:59:08.7826784Z         storage with two arguments: storage and location. The storage argument
2025-03-04T20:59:08.7827024Z         will be the initial deserialization of the storage, residing on the CPU.
2025-03-04T20:59:08.7827260Z         Each serialized storage has a location tag associated with it which
2025-03-04T20:59:08.7827482Z         identifies the device it was saved from, and this tag is the second
2025-03-04T20:59:08.7827755Z         argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2025-03-04T20:59:08.7827986Z         for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2025-03-04T20:59:08.7828242Z         :attr:`map_location` should return either ``None`` or a storage. If
2025-03-04T20:59:08.7828504Z         :attr:`map_location` returns a storage, it will be used as the final deserialized
2025-03-04T20:59:08.7828770Z         object, already moved to the right device. Otherwise, :func:`torch.load` will
2025-03-04T20:59:08.7829025Z         fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2025-03-04T20:59:08.7829126Z     
2025-03-04T20:59:08.7829370Z         If :attr:`map_location` is a :class:`torch.device` object or a string containing
2025-03-04T20:59:08.7829625Z         a device tag, it indicates the location where all tensors should be loaded.
2025-03-04T20:59:08.7829717Z     
2025-03-04T20:59:08.7829997Z         Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2025-03-04T20:59:08.7830215Z         appearing in the file (keys), to ones that specify where to put the
2025-03-04T20:59:08.7830336Z         storages (values).
2025-03-04T20:59:08.7830425Z     
2025-03-04T20:59:08.7830668Z         User extensions can register their own location tags and tagging and
2025-03-04T20:59:08.7830973Z         deserialization methods using :func:`torch.serialization.register_package`.
2025-03-04T20:59:08.7831077Z     
2025-03-04T20:59:08.7831169Z         Args:
2025-03-04T20:59:08.7831542Z             f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2025-03-04T20:59:08.7831725Z                 or a string or os.PathLike object containing a file name
2025-03-04T20:59:08.7832070Z             map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2025-03-04T20:59:08.7832175Z                 locations
2025-03-04T20:59:08.7832424Z             pickle_module: module used for unpickling metadata and objects (has to
2025-03-04T20:59:08.7832603Z                 match the :attr:`pickle_module` used to serialize file)
2025-03-04T20:59:08.7832841Z             weights_only: Indicates whether unpickler should be restricted to
2025-03-04T20:59:08.7833015Z                 loading only tensors, primitive types, dictionaries
2025-03-04T20:59:08.7833256Z                 and any types added via :func:`torch.serialization.add_safe_globals`.
2025-03-04T20:59:08.7833402Z                 See :ref:`weights-only` for more details.
2025-03-04T20:59:08.7833758Z             mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
2025-03-04T20:59:08.7834106Z                 Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2025-03-04T20:59:08.7834503Z                 are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2025-03-04T20:59:08.7834841Z                 second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2025-03-04T20:59:08.7835104Z                 tensor storages from disk to CPU memory in the first step, ``f`` is mmaped.
2025-03-04T20:59:08.7835353Z             pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2025-03-04T20:59:08.7835596Z                 :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2025-03-04T20:59:08.7835710Z                 :attr:`errors=...`.
2025-03-04T20:59:08.7835811Z     
2025-03-04T20:59:08.7835916Z         .. warning::
2025-03-04T20:59:08.7836205Z             :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2025-03-04T20:59:08.7836487Z             uses ``pickle`` module implicitly, which is known to be insecure.
2025-03-04T20:59:08.7836811Z             It is possible to construct malicious pickle data which will execute arbitrary code
2025-03-04T20:59:08.7837061Z             during unpickling. Never load data that could have come from an untrusted
2025-03-04T20:59:08.7837376Z             source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2025-03-04T20:59:08.7837506Z     
2025-03-04T20:59:08.7837616Z         .. note::
2025-03-04T20:59:08.7837888Z             When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2025-03-04T20:59:08.7838165Z             will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2025-03-04T20:59:08.7838446Z             and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2025-03-04T20:59:08.7838546Z     
2025-03-04T20:59:08.7838642Z         .. note::
2025-03-04T20:59:08.7838911Z             By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2025-03-04T20:59:08.7839140Z             case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2025-03-04T20:59:08.7839370Z             when loading files saved by Python 2 in Python 3.  If this default
2025-03-04T20:59:08.7839636Z             is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2025-03-04T20:59:08.7839901Z             these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2025-03-04T20:59:08.7840191Z             to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2025-03-04T20:59:08.7840441Z             as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2025-03-04T20:59:08.7840561Z     
2025-03-04T20:59:08.7840718Z         Example:
2025-03-04T20:59:08.7840860Z             >>> # xdoctest: +SKIP("undefined filepaths")
2025-03-04T20:59:08.7841025Z             >>> torch.load("tensors.pt", weights_only=True)
2025-03-04T20:59:08.7841149Z             # Load all tensors onto the CPU
2025-03-04T20:59:08.7841263Z             >>> torch.load(
2025-03-04T20:59:08.7841378Z             ...     "tensors.pt",
2025-03-04T20:59:08.7841530Z             ...     map_location=torch.device("cpu"),
2025-03-04T20:59:08.7841641Z             ...     weights_only=True,
2025-03-04T20:59:08.7841747Z             ... )
2025-03-04T20:59:08.7841903Z             # Load all tensors onto the CPU, using a function
2025-03-04T20:59:08.7842017Z             >>> torch.load(
2025-03-04T20:59:08.7842128Z             ...     "tensors.pt",
2025-03-04T20:59:08.7842294Z             ...     map_location=lambda storage, loc: storage,
2025-03-04T20:59:08.7842407Z             ...     weights_only=True,
2025-03-04T20:59:08.7842516Z             ... )
2025-03-04T20:59:08.7842635Z             # Load all tensors onto GPU 1
2025-03-04T20:59:08.7842749Z             >>> torch.load(
2025-03-04T20:59:08.7842854Z             ...     "tensors.pt",
2025-03-04T20:59:08.7843038Z             ...     map_location=lambda storage, loc: storage.cuda(1),
2025-03-04T20:59:08.7843150Z             ...     weights_only=True,
2025-03-04T20:59:08.7843315Z             ... )  # type: ignore[attr-defined]
2025-03-04T20:59:08.7843440Z             # Map tensors from GPU 1 to GPU 0
2025-03-04T20:59:08.7843552Z             >>> torch.load(
2025-03-04T20:59:08.7843658Z             ...     "tensors.pt",
2025-03-04T20:59:08.7843800Z             ...     map_location={"cuda:1": "cuda:0"},
2025-03-04T20:59:08.7843911Z             ...     weights_only=True,
2025-03-04T20:59:08.7844014Z             ... )
2025-03-04T20:59:08.7844144Z             # Load tensor from io.BytesIO object
2025-03-04T20:59:08.7844419Z             # Loading from a buffer setting weights_only=False, warning this can be unsafe
2025-03-04T20:59:08.7844549Z             >>> with open("tensor.pt", "rb") as f:
2025-03-04T20:59:08.7844686Z             ...     buffer = io.BytesIO(f.read())
2025-03-04T20:59:08.7844822Z             >>> torch.load(buffer, weights_only=False)
2025-03-04T20:59:08.7844996Z             # Load a module with 'ascii' encoding for unpickling
2025-03-04T20:59:08.7845255Z             # Loading from a module setting weights_only=False, warning this can be unsafe
2025-03-04T20:59:08.7845471Z             >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2025-03-04T20:59:08.7845591Z         
2025-03-04T20:59:08.7845868Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7845961Z     
2025-03-04T20:59:08.7846066Z       warnings.warn(msg)
2025-03-04T20:59:08.7846167Z     
2025-03-04T20:59:08.7846404Z --- Parse Warning: 13 / 116 ---
2025-03-04T20:59:08.7847323Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=37.
2025-03-04T20:59:08.7847598Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:59:08.7847817Z     Check if there is an available :ref:`accelerator<accelerators>`.
2025-03-04T20:59:08.7847910Z     
2025-03-04T20:59:08.7848013Z         Returns:
2025-03-04T20:59:08.7848301Z             bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2025-03-04T20:59:08.7848401Z     
2025-03-04T20:59:08.7848501Z         Example::
2025-03-04T20:59:08.7848591Z     
2025-03-04T20:59:08.7848884Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:59:08.7849008Z         
2025-03-04T20:59:08.7849624Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2025-03-04T20:59:08.7849717Z     
2025-03-04T20:59:08.7850000Z     assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:59:08.7850112Z                                             ^
2025-03-04T20:59:08.7850229Z       warnings.warn(msg)
2025-03-04T20:59:08.7850319Z     
2025-03-04T20:59:08.7850530Z --- Parse Warning: 14 / 116 ---
2025-03-04T20:59:08.7851436Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=138.
2025-03-04T20:59:08.7851704Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:59:08.7851922Z     Wait for all kernels in all streams on the given device to complete.
2025-03-04T20:59:08.7852019Z     
2025-03-04T20:59:08.7852113Z         Args:
2025-03-04T20:59:08.7852454Z             device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2025-03-04T20:59:08.7852693Z                 the current :ref:`accelerator<accelerators>` device type. If not given,
2025-03-04T20:59:08.7852910Z                 use :func:`torch.accelerator.current_device_index` by default.
2025-03-04T20:59:08.7853001Z     
2025-03-04T20:59:08.7853360Z         .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2025-03-04T20:59:08.7853451Z     
2025-03-04T20:59:08.7853558Z         Example::
2025-03-04T20:59:08.7853645Z     
2025-03-04T20:59:08.7853809Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:08.7854088Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:59:08.7854256Z             >>> start_event = torch.Event(enable_timing=True)
2025-03-04T20:59:08.7854404Z             >>> end_event = torch.Event(enable_timing=True)
2025-03-04T20:59:08.7854532Z             >>> start_event.record()
2025-03-04T20:59:08.7854781Z             >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2025-03-04T20:59:08.7854906Z             >>> sum = torch.sum(tensor)
2025-03-04T20:59:08.7855015Z             >>> end_event.record()
2025-03-04T20:59:08.7855161Z             >>> torch.accelerator.synchronize()
2025-03-04T20:59:08.7855341Z             >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2025-03-04T20:59:08.7855442Z         
2025-03-04T20:59:08.7856009Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2025-03-04T20:59:08.7856136Z     
2025-03-04T20:59:08.7856404Z     assert torch.accelerator.is_available() "No available accelerators detected."
2025-03-04T20:59:08.7856524Z                                             ^
2025-03-04T20:59:08.7856629Z       warnings.warn(msg)
2025-03-04T20:59:08.7856724Z     
2025-03-04T20:59:08.7856919Z --- Parse Warning: 15 / 116 ---
2025-03-04T20:59:08.7857875Z     /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=400.
2025-03-04T20:59:08.7858135Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:59:08.7858271Z     Retrieves the CUDA runtime API module.
2025-03-04T20:59:08.7858359Z     
2025-03-04T20:59:08.7858460Z     
2025-03-04T20:59:08.7858721Z         This function initializes the CUDA runtime environment if it is not already
2025-03-04T20:59:08.7858973Z         initialized and returns the CUDA runtime API module (_cudart). The CUDA
2025-03-04T20:59:08.7859241Z         runtime API module provides access to various CUDA runtime functions.
2025-03-04T20:59:08.7859339Z     
2025-03-04T20:59:08.7859429Z         Args:
2025-03-04T20:59:08.7859536Z             ``None``
2025-03-04T20:59:08.7859648Z     
2025-03-04T20:59:08.7859760Z         Returns:
2025-03-04T20:59:08.7859911Z             module: The CUDA runtime API module (_cudart).
2025-03-04T20:59:08.7860012Z     
2025-03-04T20:59:08.7860102Z         Raises:
2025-03-04T20:59:08.7860354Z             RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2025-03-04T20:59:08.7860725Z             AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2025-03-04T20:59:08.7860825Z     
2025-03-04T20:59:08.7860966Z         Example of CUDA operations with profiling:
2025-03-04T20:59:08.7861086Z             >>> import torch
2025-03-04T20:59:08.7861228Z             >>> from torch.cuda import cudart, check_error
2025-03-04T20:59:08.7861343Z             >>> import os
2025-03-04T20:59:08.7861430Z             >>>
2025-03-04T20:59:08.7861556Z             >>> os.environ['CUDA_PROFILE'] = '1'
2025-03-04T20:59:08.7861656Z             >>>
2025-03-04T20:59:08.7861807Z             >>> def perform_cuda_operations_with_streams():
2025-03-04T20:59:08.7861942Z             >>>     stream = torch.cuda.Stream()
2025-03-04T20:59:08.7862074Z             >>>     with torch.cuda.stream(stream):
2025-03-04T20:59:08.7862221Z             >>>         x = torch.randn(100, 100, device='cuda')
2025-03-04T20:59:08.7862356Z             >>>         y = torch.randn(100, 100, device='cuda')
2025-03-04T20:59:08.7862507Z             >>>         z = torch.mul(x, y)
2025-03-04T20:59:08.7862605Z             >>>     return z
2025-03-04T20:59:08.7862706Z             >>>
2025-03-04T20:59:08.7862825Z             >>> torch.cuda.synchronize()
2025-03-04T20:59:08.7862982Z             >>> print("====== Start nsys profiling ======")
2025-03-04T20:59:08.7863127Z             >>> check_error(cudart().cudaProfilerStart())
2025-03-04T20:59:08.7863303Z             >>> with torch.autograd.profiler.emit_nvtx():
2025-03-04T20:59:08.7863465Z             >>>     result = perform_cuda_operations_with_streams()
2025-03-04T20:59:08.7863615Z             >>>     print("CUDA operations completed.")
2025-03-04T20:59:08.7863788Z             >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2025-03-04T20:59:08.7863940Z             >>> print("====== End nsys profiling ======")
2025-03-04T20:59:08.7864032Z     
2025-03-04T20:59:08.7864254Z         To run this example and save the profiling information, execute:
2025-03-04T20:59:08.7864628Z             >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-04T20:59:08.7864730Z     
2025-03-04T20:59:08.7864984Z         This command profiles the CUDA operations in the provided script and saves
2025-03-04T20:59:08.7865227Z         the profiling information to a file named `trace_name.prof`.
2025-03-04T20:59:08.7865470Z         The `--profile-from-start off` option ensures that profiling starts only
2025-03-04T20:59:08.7865639Z         after the `cudaProfilerStart` call in the script.
2025-03-04T20:59:08.7865873Z         The `--csv` and `--print-summary` options format the profiling output as a
2025-03-04T20:59:08.7866026Z         CSV file and print a summary, respectively.
2025-03-04T20:59:08.7866278Z         The `-o` option specifies the output file name, and the `-f` option forces the
2025-03-04T20:59:08.7866452Z         overwrite of the output file if it already exists.
2025-03-04T20:59:08.7866543Z         
2025-03-04T20:59:08.7867213Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2025-03-04T20:59:08.7867304Z     
2025-03-04T20:59:08.7867673Z     $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-03-04T20:59:08.7867791Z     ^
2025-03-04T20:59:08.7867905Z       warnings.warn(msg)
2025-03-04T20:59:08.7867995Z     
2025-03-04T20:59:08.7868209Z --- Parse Warning: 16 / 116 ---
2025-03-04T20:59:08.7869111Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=105.
2025-03-04T20:59:08.7869393Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7869481Z     
2025-03-04T20:59:08.7869730Z     Append the given callback function to this ``Future``, which will be run
2025-03-04T20:59:08.7869946Z     when the ``Future`` is completed.  Multiple callbacks can be added to
2025-03-04T20:59:08.7870174Z     the same ``Future``, but the order in which they will be executed cannot
2025-03-04T20:59:08.7870371Z     be guaranteed (to enforce a certain order consider chaining:
2025-03-04T20:59:08.7870596Z     ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2025-03-04T20:59:08.7870813Z     is the reference to this ``Future``. The callback function can use the
2025-03-04T20:59:08.7871037Z     :meth:`value` method to get the value. Note that if this ``Future`` is
2025-03-04T20:59:08.7871271Z     already completed, the given callback will be run immediately inline.
2025-03-04T20:59:08.7871372Z     
2025-03-04T20:59:08.7871573Z     If the ``Future``'s value contains tensors that reside on GPUs, the
2025-03-04T20:59:08.7871810Z     callback might be invoked while the async kernels that are populating
2025-03-04T20:59:08.7872073Z     those tensors haven't yet finished executing on the device. However, the
2025-03-04T20:59:08.7872300Z     callback will be invoked with some dedicated streams set as current
2025-03-04T20:59:08.7872515Z     (fetched from a global pool) which will be synchronized with those
2025-03-04T20:59:08.7872761Z     kernels. Hence any operation performed by the callback on these tensors
2025-03-04T20:59:08.7872979Z     will be scheduled on the device after the kernels complete. In other
2025-03-04T20:59:08.7873198Z     words, as long as the callback doesn't switch streams, it can safely
2025-03-04T20:59:08.7873438Z     manipulate the result without any additional synchronization. This is
2025-03-04T20:59:08.7873786Z     similar to the non-blocking behavior of :meth:`wait`.
2025-03-04T20:59:08.7873904Z     
2025-03-04T20:59:08.7874142Z     Similarly, if the callback returns a value that contains tensors that
2025-03-04T20:59:08.7874347Z     reside on a GPU, it can do so even if the kernels that are producing
2025-03-04T20:59:08.7874589Z     these tensors are still running on the device, as long as the callback
2025-03-04T20:59:08.7874800Z     didn't change streams during its execution. If one wants to change
2025-03-04T20:59:08.7875132Z     streams, one must be careful to re-synchronize them with the original
2025-03-04T20:59:08.7875357Z     streams, that is, those that were current when the callback was invoked.
2025-03-04T20:59:08.7875461Z     
2025-03-04T20:59:08.7875553Z     Args:
2025-03-04T20:59:08.7875776Z         callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2025-03-04T20:59:08.7875895Z                                 the only argument.
2025-03-04T20:59:08.7875996Z     
2025-03-04T20:59:08.7876086Z     Returns:
2025-03-04T20:59:08.7876274Z         A new ``Future`` object that holds the return value of the
2025-03-04T20:59:08.7876459Z         ``callback`` and will be marked as completed when the given
2025-03-04T20:59:08.7876586Z         ``callback`` finishes.
2025-03-04T20:59:08.7876678Z     
2025-03-04T20:59:08.7876876Z     .. note:: Note that if the callback function throws, either
2025-03-04T20:59:08.7877095Z         through the original future being completed with an exception and
2025-03-04T20:59:08.7877307Z         calling ``fut.wait()``, or through other code in the callback, the
2025-03-04T20:59:08.7877518Z         future returned by ``then`` will be marked appropriately with the
2025-03-04T20:59:08.7877781Z         encountered error. However, if this callback later completes
2025-03-04T20:59:08.7878043Z         additional futures, those futures are not marked as completed with
2025-03-04T20:59:08.7878269Z         an error and the user is responsible for handling completion/waiting
2025-03-04T20:59:08.7878393Z         on those futures independently.
2025-03-04T20:59:08.7878497Z     
2025-03-04T20:59:08.7878597Z     Example::
2025-03-04T20:59:08.7878765Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-04T20:59:08.7878873Z         >>> def callback(fut):
2025-03-04T20:59:08.7879033Z         ...     print(f"RPC return value is {fut.wait()}.")
2025-03-04T20:59:08.7879155Z         >>> fut = torch.futures.Future()
2025-03-04T20:59:08.7879348Z         >>> # The inserted callback will print the return value when
2025-03-04T20:59:08.7879486Z         >>> # receiving the response from "worker1"
2025-03-04T20:59:08.7879617Z         >>> cb_fut = fut.then(callback)
2025-03-04T20:59:08.7879731Z         >>> chain_cb_fut = cb_fut.then(
2025-03-04T20:59:08.7879891Z         ...     lambda x : print(f"Chained cb done. {x.wait()}")
2025-03-04T20:59:08.7879994Z         ... )
2025-03-04T20:59:08.7880103Z         >>> fut.set_result(5)
2025-03-04T20:59:08.7880225Z         RPC return value is 5.
2025-03-04T20:59:08.7880335Z         Chained cb done. None
2025-03-04T20:59:08.7880440Z     
2025-03-04T20:59:08.7880706Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7880808Z     
2025-03-04T20:59:08.7880918Z       warnings.warn(msg)
2025-03-04T20:59:08.7881063Z     
2025-03-04T20:59:08.7881285Z --- Parse Warning: 17 / 116 ---
2025-03-04T20:59:08.7882203Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=213.
2025-03-04T20:59:08.7882482Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7882588Z     
2025-03-04T20:59:08.7882808Z     Set the result for this ``Future``, which will mark this ``Future`` as
2025-03-04T20:59:08.7883048Z     completed and trigger all attached callbacks. Note that a ``Future``
2025-03-04T20:59:08.7883175Z     cannot be marked completed twice.
2025-03-04T20:59:08.7883282Z     
2025-03-04T20:59:08.7883510Z     If the result contains tensors that reside on GPUs, this method can be
2025-03-04T20:59:08.7883743Z     called even if the asynchronous kernels that are populating those
2025-03-04T20:59:08.7883975Z     tensors haven't yet completed running on the device, provided that the
2025-03-04T20:59:08.7884227Z     streams on which those kernels were enqueued are set as the current ones
2025-03-04T20:59:08.7884476Z     when this method is called. Put simply, it's safe to call this method
2025-03-04T20:59:08.7884714Z     immediately after launching those kernels, without any additional
2025-03-04T20:59:08.7884955Z     synchronization, as long as one doesn't change streams in between. This
2025-03-04T20:59:08.7885200Z     method will record events on all the relevant current streams and will
2025-03-04T20:59:08.7885414Z     use them to ensure proper scheduling for all the consumers of this
2025-03-04T20:59:08.7885526Z     ``Future``.
2025-03-04T20:59:08.7885618Z     
2025-03-04T20:59:08.7885726Z     Args:
2025-03-04T20:59:08.7885900Z         result (object): the result object of this ``Future``.
2025-03-04T20:59:08.7886003Z     
2025-03-04T20:59:08.7886102Z     Example::
2025-03-04T20:59:08.7886276Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-03-04T20:59:08.7886384Z         >>> import threading
2025-03-04T20:59:08.7886495Z         >>> import time
2025-03-04T20:59:08.7886618Z         >>> def slow_set_future(fut, value):
2025-03-04T20:59:08.7886741Z         ...     time.sleep(0.5)
2025-03-04T20:59:08.7886856Z         ...     fut.set_result(value)
2025-03-04T20:59:08.7887016Z         >>> fut = torch.futures.Future()
2025-03-04T20:59:08.7887128Z         >>> t = threading.Thread(
2025-03-04T20:59:08.7887252Z         ...     target=slow_set_future,
2025-03-04T20:59:08.7887398Z         ...     args=(fut, torch.ones(2) * 3)
2025-03-04T20:59:08.7887504Z         ... )
2025-03-04T20:59:08.7887598Z         >>> t.start()
2025-03-04T20:59:08.7887708Z         >>> print(fut.wait())
2025-03-04T20:59:08.7887819Z         tensor([3., 3.])
2025-03-04T20:59:08.7887913Z         >>> t.join()
2025-03-04T20:59:08.7888013Z     
2025-03-04T20:59:08.7888280Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7888382Z     
2025-03-04T20:59:08.7888487Z       warnings.warn(msg)
2025-03-04T20:59:08.7888586Z     
2025-03-04T20:59:08.7888782Z --- Parse Warning: 18 / 116 ---
2025-03-04T20:59:08.7889677Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144.
2025-03-04T20:59:08.7889952Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7890194Z     Compiles compute shader from source and allows one to invoke kernels
2025-03-04T20:59:08.7890356Z         defined there from the comfort of Python runtime
2025-03-04T20:59:08.7890465Z         Example::
2025-03-04T20:59:08.7890556Z     
2025-03-04T20:59:08.7890716Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)
2025-03-04T20:59:08.7890873Z             >>> lib = torch.mps._compile_shader(
2025-03-04T20:59:08.7891281Z             ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"
2025-03-04T20:59:08.7891377Z             ...  )
2025-03-04T20:59:08.7891521Z             >>> x = torch.zeros(16, device="mps")
2025-03-04T20:59:08.7891630Z             >>> lib.full(x, 3.14)
2025-03-04T20:59:08.7891737Z         
2025-03-04T20:59:08.7891999Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7892102Z     
2025-03-04T20:59:08.7892210Z       warnings.warn(msg)
2025-03-04T20:59:08.7892316Z     
2025-03-04T20:59:08.7892511Z --- Parse Warning: 19 / 116 ---
2025-03-04T20:59:08.7893364Z     /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.
2025-03-04T20:59:08.7893641Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7893826Z     Return the sum of each row of the given sparse tensor.
2025-03-04T20:59:08.7893917Z     
2025-03-04T20:59:08.7894164Z         Returns the sum of each row of the sparse tensor :attr:`input` in the given
2025-03-04T20:59:08.7894398Z         dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2025-03-04T20:59:08.7894635Z         reduce over all of them. When sum over all ``sparse_dim``, this method
2025-03-04T20:59:08.7894796Z         returns a dense tensor instead of a sparse tensor.
2025-03-04T20:59:08.7894900Z     
2025-03-04T20:59:08.7895168Z         All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2025-03-04T20:59:08.7895385Z         tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2025-03-04T20:59:08.7895475Z     
2025-03-04T20:59:08.7895717Z         During backward, only gradients at ``nnz`` locations of :attr:`input`
2025-03-04T20:59:08.7895969Z         will propagate back. Note that the gradients of :attr:`input` is coalesced.
2025-03-04T20:59:08.7896070Z     
2025-03-04T20:59:08.7896162Z         Args:
2025-03-04T20:59:08.7896310Z             input (Tensor): the input sparse tensor
2025-03-04T20:59:08.7896596Z             dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2025-03-04T20:59:08.7896745Z                 over all dims.
2025-03-04T20:59:08.7897013Z             dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2025-03-04T20:59:08.7897181Z                 Default: dtype of :attr:`input`.
2025-03-04T20:59:08.7897273Z     
2025-03-04T20:59:08.7897382Z         Example::
2025-03-04T20:59:08.7897472Z     
2025-03-04T20:59:08.7897583Z             >>> nnz = 3
2025-03-04T20:59:08.7897691Z             >>> dims = [5, 5, 2, 3]
2025-03-04T20:59:08.7897954Z             >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2025-03-04T20:59:08.7898155Z                                torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2025-03-04T20:59:08.7898307Z             >>> V = torch.randn(nnz, dims[2], dims[3])
2025-03-04T20:59:08.7898425Z             >>> size = torch.Size(dims)
2025-03-04T20:59:08.7898580Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:08.7898736Z             >>> S = torch.sparse_coo_tensor(I, V, size)
2025-03-04T20:59:08.7898832Z             >>> S
2025-03-04T20:59:08.7898970Z             tensor(indices=tensor([[2, 0, 3],
2025-03-04T20:59:08.7899081Z                                    [2, 4, 1]]),
2025-03-04T20:59:08.7899237Z                    values=tensor([[[-0.6438, -1.6467,  1.4004],
2025-03-04T20:59:08.7899359Z                                    [ 0.3411,  0.0918, -0.2312]],
2025-03-04T20:59:08.7899462Z     
2025-03-04T20:59:08.7899591Z                                   [[ 0.5348,  0.0634, -2.0494],
2025-03-04T20:59:08.7899722Z                                    [-0.7125, -1.0646,  2.1844]],
2025-03-04T20:59:08.7899849Z     
2025-03-04T20:59:08.7899980Z                                   [[ 0.1276,  0.1874, -0.6334],
2025-03-04T20:59:08.7900101Z                                    [-1.9682, -0.5340,  0.7483]]]),
2025-03-04T20:59:08.7900270Z                    size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2025-03-04T20:59:08.7900359Z     
2025-03-04T20:59:08.7900574Z             # when sum over only part of sparse_dims, return a sparse tensor
2025-03-04T20:59:08.7900698Z             >>> torch.sparse.sum(S, [1, 3])
2025-03-04T20:59:08.7900875Z             tensor(indices=tensor([[0, 2, 3]]),
2025-03-04T20:59:08.7901000Z                    values=tensor([[-1.4512,  0.4073],
2025-03-04T20:59:08.7901127Z                                   [-0.8901,  0.2017],
2025-03-04T20:59:08.7901241Z                                   [-0.3183, -1.7539]]),
2025-03-04T20:59:08.7901399Z                    size=(5, 2), nnz=3, layout=torch.sparse_coo)
2025-03-04T20:59:08.7901489Z     
2025-03-04T20:59:08.7901671Z             # when sum over all sparse dim, return a dense tensor
2025-03-04T20:59:08.7901786Z             # with summed dims squeezed
2025-03-04T20:59:08.7901925Z             >>> torch.sparse.sum(S, [0, 1, 3])
2025-03-04T20:59:08.7902033Z             tensor([-2.6596, -1.1450])
2025-03-04T20:59:08.7902165Z         
2025-03-04T20:59:08.7902428Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7902531Z     
2025-03-04T20:59:08.7902638Z       warnings.warn(msg)
2025-03-04T20:59:08.7902739Z     
2025-03-04T20:59:08.7902944Z --- Parse Warning: 20 / 116 ---
2025-03-04T20:59:08.7903804Z     /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.
2025-03-04T20:59:08.7904076Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7904177Z     
2025-03-04T20:59:08.7904407Z     vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2025-03-04T20:59:08.7904630Z     maps ``func`` over some dimension of the inputs. Semantically, vmap
2025-03-04T20:59:08.7904855Z     pushes the map into PyTorch operations called by ``func``, effectively
2025-03-04T20:59:08.7904991Z     vectorizing those operations.
2025-03-04T20:59:08.7905113Z     
2025-03-04T20:59:08.7905347Z     vmap is useful for handling batch dimensions: one can write a function
2025-03-04T20:59:08.7905556Z     ``func`` that runs on examples and then lift it to a function that can
2025-03-04T20:59:08.7905814Z     take batches of examples with ``vmap(func)``. vmap can also be used to
2025-03-04T20:59:08.7905996Z     compute batched gradients when composed with autograd.
2025-03-04T20:59:08.7906097Z     
2025-03-04T20:59:08.7906194Z     .. note::
2025-03-04T20:59:08.7906401Z         :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2025-03-04T20:59:08.7906541Z         convenience. Use whichever one you'd like.
2025-03-04T20:59:08.7906644Z     
2025-03-04T20:59:08.7906739Z     Args:
2025-03-04T20:59:08.7906976Z         func (function): A Python function that takes one or more arguments.
2025-03-04T20:59:08.7907102Z             Must return one or more Tensors.
2025-03-04T20:59:08.7907319Z         in_dims (int or nested structure): Specifies which dimension of the
2025-03-04T20:59:08.7907519Z             inputs should be mapped over. ``in_dims`` should have a
2025-03-04T20:59:08.7907723Z             structure like the inputs. If the ``in_dim`` for a particular
2025-03-04T20:59:08.7907934Z             input is None, then that indicates there is no map dimension.
2025-03-04T20:59:08.7908035Z             Default: 0.
2025-03-04T20:59:08.7908252Z         out_dims (int or Tuple[int]): Specifies where the mapped dimension
2025-03-04T20:59:08.7908454Z             should appear in the outputs. If ``out_dims`` is a Tuple, then
2025-03-04T20:59:08.7908631Z             it should have one element per output. Default: 0.
2025-03-04T20:59:08.7908871Z         randomness (str): Specifies whether the randomness in this
2025-03-04T20:59:08.7909103Z             vmap should be the same or different across batches. If 'different',
2025-03-04T20:59:08.7909316Z             the randomness for each batch will be different. If 'same', the
2025-03-04T20:59:08.7909554Z             randomness will be the same across batches. If 'error', any calls to
2025-03-04T20:59:08.7909787Z             random functions will error. Default: 'error'. WARNING: this flag
2025-03-04T20:59:08.7910002Z             only applies to random PyTorch operations and does not apply to
2025-03-04T20:59:08.7910158Z             Python's random module or numpy randomness.
2025-03-04T20:59:08.7910395Z         chunk_size (None or int): If None (default), apply a single vmap over inputs.
2025-03-04T20:59:08.7910634Z             If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2025-03-04T20:59:08.7910905Z             Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2025-03-04T20:59:08.7911193Z             If you run into memory issues computing the vmap, please try a non-None chunk_size.
2025-03-04T20:59:08.7911285Z     
2025-03-04T20:59:08.7911408Z     Returns:
2025-03-04T20:59:08.7911632Z         Returns a new "batched" function. It takes the same inputs as
2025-03-04T20:59:08.7911829Z         ``func``, except each input has an extra dimension at the index
2025-03-04T20:59:08.7912041Z         specified by ``in_dims``. It takes returns the same outputs as
2025-03-04T20:59:08.7912238Z         ``func``, except each output has an extra dimension at the index
2025-03-04T20:59:08.7912367Z         specified by ``out_dims``.
2025-03-04T20:59:08.7912457Z     
2025-03-04T20:59:08.7912567Z     .. warning:
2025-03-04T20:59:08.7912777Z         :func:`vmap` works best with functional-style code. Please do not
2025-03-04T20:59:08.7912985Z         perform any side-effects in ``func``, with the exception of
2025-03-04T20:59:08.7913233Z         in-place PyTorch operations. Examples of side-effects include mutating
2025-03-04T20:59:08.7913483Z         Python data structures and assigning values to variables not captured
2025-03-04T20:59:08.7913584Z         in ``func``.
2025-03-04T20:59:08.7913691Z     
2025-03-04T20:59:08.7913938Z     One example of using :func:`vmap` is to compute batched dot products. PyTorch
2025-03-04T20:59:08.7914208Z     doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2025-03-04T20:59:08.7914440Z     rummaging through docs, use :func:`vmap` to construct a new function.
2025-03-04T20:59:08.7914545Z     
2025-03-04T20:59:08.7914723Z         >>> torch.dot                            # [D], [D] -> []
2025-03-04T20:59:08.7914950Z         >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2025-03-04T20:59:08.7915091Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-04T20:59:08.7915214Z         >>> batched_dot(x, y)
2025-03-04T20:59:08.7915306Z     
2025-03-04T20:59:08.7915561Z     :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2025-03-04T20:59:08.7915683Z     model authoring experience.
2025-03-04T20:59:08.7915789Z     
2025-03-04T20:59:08.7915940Z         >>> batch_size, feature_size = 3, 5
2025-03-04T20:59:08.7916201Z         >>> weights = torch.randn(feature_size, requires_grad=True)
2025-03-04T20:59:08.7916299Z         >>>
2025-03-04T20:59:08.7916426Z         >>> def model(feature_vec):
2025-03-04T20:59:08.7916572Z         >>>     # Very simple linear model with activation
2025-03-04T20:59:08.7916724Z         >>>     return feature_vec.dot(weights).relu()
2025-03-04T20:59:08.7916816Z         >>>
2025-03-04T20:59:08.7916995Z         >>> examples = torch.randn(batch_size, feature_size)
2025-03-04T20:59:08.7917128Z         >>> result = torch.vmap(model)(examples)
2025-03-04T20:59:08.7917230Z     
2025-03-04T20:59:08.7917490Z     :func:`vmap` can also help vectorize computations that were previously difficult
2025-03-04T20:59:08.7917784Z     or impossible to batch. One example is higher-order gradient computation.
2025-03-04T20:59:08.7918025Z     The PyTorch autograd engine computes vjps (vector-Jacobian products).
2025-03-04T20:59:08.7918276Z     Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2025-03-04T20:59:08.7918535Z     requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2025-03-04T20:59:08.7918795Z     we can vectorize the whole computation, computing the Jacobian in a single
2025-03-04T20:59:08.7918909Z     call to ``autograd.grad``.
2025-03-04T20:59:08.7919011Z     
2025-03-04T20:59:08.7919109Z         >>> # Setup
2025-03-04T20:59:08.7919217Z         >>> N = 5
2025-03-04T20:59:08.7919322Z         >>> f = lambda x: x ** 2
2025-03-04T20:59:08.7919469Z         >>> x = torch.randn(N, requires_grad=True)
2025-03-04T20:59:08.7919563Z         >>> y = f(x)
2025-03-04T20:59:08.7919667Z         >>> I_N = torch.eye(N)
2025-03-04T20:59:08.7919773Z         >>>
2025-03-04T20:59:08.7919887Z         >>> # Sequential approach
2025-03-04T20:59:08.7920124Z         >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2025-03-04T20:59:08.7920255Z         >>>                  for v in I_N.unbind()]
2025-03-04T20:59:08.7920428Z         >>> jacobian = torch.stack(jacobian_rows)
2025-03-04T20:59:08.7920518Z         >>>
2025-03-04T20:59:08.7920659Z         >>> # vectorized gradient computation
2025-03-04T20:59:08.7920765Z         >>> def get_vjp(v):
2025-03-04T20:59:08.7920912Z         >>>     return torch.autograd.grad(y, x, v)
2025-03-04T20:59:08.7921042Z         >>> jacobian = torch.vmap(get_vjp)(I_N)
2025-03-04T20:59:08.7921147Z     
2025-03-04T20:59:08.7921422Z     :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2025-03-04T20:59:08.7921527Z     
2025-03-04T20:59:08.7921681Z         >>> torch.dot                            # [D], [D] -> []
2025-03-04T20:59:08.7921978Z         >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2025-03-04T20:59:08.7922129Z         >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2025-03-04T20:59:08.7922279Z         >>> batched_dot(x, y) # tensor of size [2, 3]
2025-03-04T20:59:08.7922369Z     
2025-03-04T20:59:08.7922635Z     If the inputs are not batched along the first dimension, ``in_dims`` specifies
2025-03-04T20:59:08.7922798Z     the dimension that each inputs are batched along as
2025-03-04T20:59:08.7922930Z     
2025-03-04T20:59:08.7923122Z         >>> torch.dot                            # [N], [N] -> []
2025-03-04T20:59:08.7923386Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2025-03-04T20:59:08.7923551Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-03-04T20:59:08.7923813Z         >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension
2025-03-04T20:59:08.7923902Z     
2025-03-04T20:59:08.7924178Z     If there are multiple inputs each of which is batched along different dimensions,
2025-03-04T20:59:08.7924391Z     ``in_dims`` must be a tuple with the batch dimension for each input as
2025-03-04T20:59:08.7924496Z     
2025-03-04T20:59:08.7924650Z         >>> torch.dot                            # [D], [D] -> []
2025-03-04T20:59:08.7924905Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2025-03-04T20:59:08.7925044Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-04T20:59:08.7925305Z         >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
2025-03-04T20:59:08.7925394Z     
2025-03-04T20:59:08.7925655Z     If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2025-03-04T20:59:08.7925774Z     matching the shape of the input:
2025-03-04T20:59:08.7925876Z     
2025-03-04T20:59:08.7926026Z         >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
2025-03-04T20:59:08.7926169Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-03-04T20:59:08.7926277Z         >>> input = {'x': x, 'y': y}
2025-03-04T20:59:08.7926505Z         >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
2025-03-04T20:59:08.7926616Z         >>> batched_dot(input)
2025-03-04T20:59:08.7926719Z     
2025-03-04T20:59:08.7927004Z     By default, the output is batched along the first dimension. However, it can be batched
2025-03-04T20:59:08.7927153Z     along any dimension by using ``out_dims``
2025-03-04T20:59:08.7927244Z     
2025-03-04T20:59:08.7927364Z         >>> f = lambda x: x ** 2
2025-03-04T20:59:08.7927472Z         >>> x = torch.randn(2, 5)
2025-03-04T20:59:08.7927620Z         >>> batched_pow = torch.vmap(f, out_dims=1)
2025-03-04T20:59:08.7927730Z         >>> batched_pow(x) # [5, 2]
2025-03-04T20:59:08.7927819Z     
2025-03-04T20:59:08.7928129Z     For any function that uses kwargs, the returned function will not batch the kwargs but will
2025-03-04T20:59:08.7928228Z     accept kwargs
2025-03-04T20:59:08.7928328Z     
2025-03-04T20:59:08.7928438Z         >>> x = torch.randn([2, 5])
2025-03-04T20:59:08.7928560Z         >>> def fn(x, scale=4.):
2025-03-04T20:59:08.7928665Z         >>>   return x * scale
2025-03-04T20:59:08.7928767Z         >>>
2025-03-04T20:59:08.7928889Z         >>> batched_pow = torch.vmap(fn)
2025-03-04T20:59:08.7929079Z         >>> assert torch.allclose(batched_pow(x), x * 4)
2025-03-04T20:59:08.7929311Z         >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
2025-03-04T20:59:08.7929414Z     
2025-03-04T20:59:08.7929511Z     .. note::
2025-03-04T20:59:08.7929753Z         vmap does not provide general autobatching or handle variable-length
2025-03-04T20:59:08.7929874Z         sequences out of the box.
2025-03-04T20:59:08.7929978Z     
2025-03-04T20:59:08.7930240Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7930344Z     
2025-03-04T20:59:08.7930450Z       warnings.warn(msg)
2025-03-04T20:59:08.7930550Z     
2025-03-04T20:59:08.7930773Z --- Parse Warning: 21 / 116 ---
2025-03-04T20:59:08.7931658Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=21.
2025-03-04T20:59:08.7931933Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7932238Z     Create a custom operator whose implementation is backed by 1+ triton kernels.
2025-03-04T20:59:08.7932328Z     
2025-03-04T20:59:08.7932558Z         This is a more structured way of using triton kernels with PyTorch.
2025-03-04T20:59:08.7932852Z         Prefer using triton kernels with no ``torch.library`` custom operator wrappers
2025-03-04T20:59:08.7933170Z         (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because
2025-03-04T20:59:08.7933274Z         that is simpler;
2025-03-04T20:59:08.7933550Z         only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you
2025-03-04T20:59:08.7933787Z         want to create an operator that behaves like PyTorch built-in operators.
2025-03-04T20:59:08.7934022Z         For example, you may use a ``torch.library`` wrapper API to define the
2025-03-04T20:59:08.7934248Z         behavior of the triton kernel when passed a tensor subclass or under
2025-03-04T20:59:08.7934373Z         a TorchDispatchMode.
2025-03-04T20:59:08.7934464Z     
2025-03-04T20:59:08.7934735Z         Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op`
2025-03-04T20:59:08.7934847Z         when the implementation
2025-03-04T20:59:08.7935086Z         consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2025-03-04T20:59:08.7935265Z         custom operators as opaque (:func:`torch.compile` and
2025-03-04T20:59:08.7935519Z         :func:`torch.export.export` will never trace into them), but ``triton_op``
2025-03-04T20:59:08.7935750Z         makes the implementation visible to these subsystems, allowing them
2025-03-04T20:59:08.7935917Z         to optimize the triton kernel(s).
2025-03-04T20:59:08.7936009Z     
2025-03-04T20:59:08.7936220Z         Note that ``fn`` must only consist of calls to PyTorch-understood
2025-03-04T20:59:08.7936454Z         operators and triton kernels. Any triton kernels called inside ``fn``
2025-03-04T20:59:08.7936669Z         must be wrapped in a call to :func:`torch.library.wrap_triton`.
2025-03-04T20:59:08.7936761Z     
2025-03-04T20:59:08.7936866Z         Args:
2025-03-04T20:59:08.7937100Z             name (str): A name for the custom op that looks like "{namespace}::{name}",
2025-03-04T20:59:08.7937337Z                 e.g. "mylib::my_linear". The name is used as the op's stable identifier
2025-03-04T20:59:08.7937513Z                 in PyTorch subsystems (e.g. torch.export, FX graphs).
2025-03-04T20:59:08.7937853Z                 To avoid name collisions, please use your project name as the namespace;
2025-03-04T20:59:08.7938072Z                 e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2025-03-04T20:59:08.7938374Z             mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2025-03-04T20:59:08.7938621Z                 This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2025-03-04T20:59:08.7938940Z                 it pessimistically assumes that all inputs to the operator are being mutated.
2025-03-04T20:59:08.7939140Z             schema (None | str): A schema string for the operator. If None
2025-03-04T20:59:08.7939418Z                 (recommended) we'll infer a schema for the operator from its type
2025-03-04T20:59:08.7939635Z                 annotations. We recommend letting us infer a schema unless you
2025-03-04T20:59:08.7939776Z                 have a specific reason not to.
2025-03-04T20:59:08.7939938Z                 Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2025-03-04T20:59:08.7940040Z     
2025-03-04T20:59:08.7940139Z         Example::
2025-03-04T20:59:08.7940236Z     
2025-03-04T20:59:08.7940387Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:08.7940503Z             >>> import torch
2025-03-04T20:59:08.7940666Z             >>> from torch.library import triton_op, wrap_triton
2025-03-04T20:59:08.7940770Z             >>>
2025-03-04T20:59:08.7940876Z             >>> import triton
2025-03-04T20:59:08.7941022Z             >>> from triton import language as tl
2025-03-04T20:59:08.7941145Z             >>>
2025-03-04T20:59:08.7941258Z             >>> @triton.jit
2025-03-04T20:59:08.7941362Z             >>> def add_kernel(
2025-03-04T20:59:08.7941461Z             >>>     in_ptr0,
2025-03-04T20:59:08.7941614Z             >>>     in_ptr1,
2025-03-04T20:59:08.7941713Z             >>>     out_ptr,
2025-03-04T20:59:08.7941827Z             >>>     n_elements,
2025-03-04T20:59:08.7941958Z             >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-04T20:59:08.7942066Z             >>> ):
2025-03-04T20:59:08.7942193Z             >>>     pid = tl.program_id(axis=0)
2025-03-04T20:59:08.7942333Z             >>>     block_start = pid * BLOCK_SIZE
2025-03-04T20:59:08.7942494Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-04T20:59:08.7942634Z             >>>     mask = offsets < n_elements
2025-03-04T20:59:08.7942779Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-04T20:59:08.7942932Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-04T20:59:08.7943044Z             >>>     output = x + y
2025-03-04T20:59:08.7943215Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-04T20:59:08.7943309Z             >>>
2025-03-04T20:59:08.7943468Z             >>> @triton_op("mylib::add", mutates_args={})
2025-03-04T20:59:08.7943661Z             >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2025-03-04T20:59:08.7943805Z             >>>     output = torch.empty_like(x)
2025-03-04T20:59:08.7943931Z             >>>     n_elements = output.numel()
2025-03-04T20:59:08.7944037Z             >>>
2025-03-04T20:59:08.7944173Z             >>>     def grid(meta):
2025-03-04T20:59:08.7944376Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-04T20:59:08.7944472Z             >>>
2025-03-04T20:59:08.7944680Z             >>>     # NB: we need to wrap the triton kernel in a call to wrap_triton
2025-03-04T20:59:08.7944877Z             >>>     wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2025-03-04T20:59:08.7945002Z             >>>     return output
2025-03-04T20:59:08.7945096Z             >>>
2025-03-04T20:59:08.7945215Z             >>> @torch.compile
2025-03-04T20:59:08.7945321Z             >>> def f(x, y):
2025-03-04T20:59:08.7945444Z             >>>     return add(x, y)
2025-03-04T20:59:08.7945538Z             >>>
2025-03-04T20:59:08.7945679Z             >>> x = torch.randn(3, device="cuda")
2025-03-04T20:59:08.7945802Z             >>> y = torch.randn(3, device="cuda")
2025-03-04T20:59:08.7945905Z             >>>
2025-03-04T20:59:08.7946001Z             >>> z = f(x, y)
2025-03-04T20:59:08.7946149Z             >>> assert torch.allclose(z, x + y)
2025-03-04T20:59:08.7946236Z     
2025-03-04T20:59:08.7946329Z         
2025-03-04T20:59:08.7946606Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7946722Z     
2025-03-04T20:59:08.7946842Z       warnings.warn(msg)
2025-03-04T20:59:08.7946932Z     
2025-03-04T20:59:08.7947160Z --- Parse Warning: 22 / 116 ---
2025-03-04T20:59:08.7948046Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=202.
2025-03-04T20:59:08.7948332Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7948533Z     Allows capture of a triton kernel into a graph via make_fx or
2025-03-04T20:59:08.7948668Z         non-strict ``torch.export``.
2025-03-04T20:59:08.7948756Z     
2025-03-04T20:59:08.7948966Z         These technologies perform Dispatcher-based tracing (via
2025-03-04T20:59:08.7949176Z         ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2025-03-04T20:59:08.7949396Z         The ``wrap_triton`` API wraps a triton kernel into a callable that
2025-03-04T20:59:08.7949525Z         can actually be traced into a graph.
2025-03-04T20:59:08.7949652Z     
2025-03-04T20:59:08.7949870Z         Please use this API together with :func:`torch.library.triton_op`.
2025-03-04T20:59:08.7949971Z     
2025-03-04T20:59:08.7950065Z         Examples:
2025-03-04T20:59:08.7950165Z     
2025-03-04T20:59:08.7950366Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.7950486Z             >>> import torch
2025-03-04T20:59:08.7950590Z             >>> import triton
2025-03-04T20:59:08.7950732Z             >>> from triton import language as tl
2025-03-04T20:59:08.7950923Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-03-04T20:59:08.7951076Z             >>> from torch.library import wrap_triton
2025-03-04T20:59:08.7951169Z             >>>
2025-03-04T20:59:08.7951282Z             >>> @triton.jit
2025-03-04T20:59:08.7951385Z             >>> def add_kernel(
2025-03-04T20:59:08.7951498Z             >>>     in_ptr0,
2025-03-04T20:59:08.7951598Z             >>>     in_ptr1,
2025-03-04T20:59:08.7951711Z             >>>     out_ptr,
2025-03-04T20:59:08.7951814Z             >>>     n_elements,
2025-03-04T20:59:08.7951949Z             >>>     BLOCK_SIZE: "tl.constexpr",
2025-03-04T20:59:08.7952042Z             >>> ):
2025-03-04T20:59:08.7952174Z             >>>     pid = tl.program_id(axis=0)
2025-03-04T20:59:08.7952301Z             >>>     block_start = pid * BLOCK_SIZE
2025-03-04T20:59:08.7952472Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-03-04T20:59:08.7952592Z             >>>     mask = offsets < n_elements
2025-03-04T20:59:08.7952746Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-03-04T20:59:08.7952910Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-03-04T20:59:08.7953030Z             >>>     output = x + y
2025-03-04T20:59:08.7953186Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-03-04T20:59:08.7953277Z             >>>
2025-03-04T20:59:08.7953394Z             >>> def add(x, y):
2025-03-04T20:59:08.7953518Z             >>>     output = torch.empty_like(x)
2025-03-04T20:59:08.7953658Z             >>>     n_elements = output.numel()
2025-03-04T20:59:08.7953749Z             >>>
2025-03-04T20:59:08.7953872Z             >>>     def grid_fn(meta):
2025-03-04T20:59:08.7954055Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-03-04T20:59:08.7954162Z             >>>
2025-03-04T20:59:08.7954371Z             >>>     wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2025-03-04T20:59:08.7954490Z             >>>     return output
2025-03-04T20:59:08.7954581Z             >>>
2025-03-04T20:59:08.7954722Z             >>> x = torch.randn(3, device="cuda")
2025-03-04T20:59:08.7954850Z             >>> y = torch.randn(3, device="cuda")
2025-03-04T20:59:08.7954975Z             >>> gm = make_fx(add)(x, y)
2025-03-04T20:59:08.7955082Z             >>> print(gm.code)
2025-03-04T20:59:08.7955244Z             >>> # def forward(self, x_1, y_1):
2025-03-04T20:59:08.7955491Z             >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2025-03-04T20:59:08.7955759Z             >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2025-03-04T20:59:08.7955901Z             >>> #         kernel_idx = 0, constant_args_idx = 0,
2025-03-04T20:59:08.7956038Z             >>> #         grid = [(1, 1, 1)], kwargs = {
2025-03-04T20:59:08.7956202Z             >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2025-03-04T20:59:08.7956350Z             >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2025-03-04T20:59:08.7956452Z             >>> #         })
2025-03-04T20:59:08.7956578Z             >>> #     return empty_like
2025-03-04T20:59:08.7956671Z     
2025-03-04T20:59:08.7956773Z         
2025-03-04T20:59:08.7957036Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7957138Z     
2025-03-04T20:59:08.7957248Z       warnings.warn(msg)
2025-03-04T20:59:08.7957350Z     
2025-03-04T20:59:08.7957551Z --- Parse Warning: 23 / 116 ---
2025-03-04T20:59:08.7958566Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=326.
2025-03-04T20:59:08.7958840Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7958946Z     
2025-03-04T20:59:08.7959162Z     Raises an AssertionError if two items are not equal up to desired
2025-03-04T20:59:08.7959273Z     precision.
2025-03-04T20:59:08.7959363Z     
2025-03-04T20:59:08.7959561Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-03-04T20:59:08.7959750Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-04T20:59:08.7959959Z               instead of this function for more consistent floating point
2025-03-04T20:59:08.7960066Z               comparisons.
2025-03-04T20:59:08.7960169Z     
2025-03-04T20:59:08.7960398Z     The test verifies that the elements of `actual` and `desired` satisfy.
2025-03-04T20:59:08.7960504Z     
2025-03-04T20:59:08.7960673Z         ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2025-03-04T20:59:08.7960776Z     
2025-03-04T20:59:08.7961014Z     That is a looser test than originally documented, but agrees with what the
2025-03-04T20:59:08.7961274Z     actual implementation in `assert_array_almost_equal` did up to rounding
2025-03-04T20:59:08.7961517Z     vagaries. An exception is raised at conflicting values. For ndarrays this
2025-03-04T20:59:08.7961656Z     delegates to assert_array_almost_equal
2025-03-04T20:59:08.7961769Z     
2025-03-04T20:59:08.7961870Z     Parameters
2025-03-04T20:59:08.7961975Z     ----------
2025-03-04T20:59:08.7962079Z     actual : array_like
2025-03-04T20:59:08.7962196Z         The object to check.
2025-03-04T20:59:08.7962301Z     desired : array_like
2025-03-04T20:59:08.7962422Z         The expected object.
2025-03-04T20:59:08.7962528Z     decimal : int, optional
2025-03-04T20:59:08.7962666Z         Desired precision, default is 7.
2025-03-04T20:59:08.7962775Z     err_msg : str, optional
2025-03-04T20:59:08.7962978Z         The error message to be printed in case of failure.
2025-03-04T20:59:08.7963089Z     verbose : bool, optional
2025-03-04T20:59:08.7963315Z         If True, the conflicting values are appended to the error message.
2025-03-04T20:59:08.7963403Z     
2025-03-04T20:59:08.7963508Z     Raises
2025-03-04T20:59:08.7963602Z     ------
2025-03-04T20:59:08.7963715Z     AssertionError
2025-03-04T20:59:08.7963920Z       If actual and desired are not equal up to specified precision.
2025-03-04T20:59:08.7964025Z     
2025-03-04T20:59:08.7964117Z     See Also
2025-03-04T20:59:08.7964224Z     --------
2025-03-04T20:59:08.7964470Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:59:08.7964648Z                      relative and/or absolute precision.
2025-03-04T20:59:08.7964868Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:59:08.7964971Z     
2025-03-04T20:59:08.7965066Z     Examples
2025-03-04T20:59:08.7965173Z     --------
2025-03-04T20:59:08.7965347Z     >>> from torch._numpy.testing import assert_almost_equal
2025-03-04T20:59:08.7965491Z     >>> assert_almost_equal(2.3333333333333, 2.33333334)
2025-03-04T20:59:08.7965688Z     >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2025-03-04T20:59:08.7965811Z     Traceback (most recent call last):
2025-03-04T20:59:08.7965913Z         ...
2025-03-04T20:59:08.7966016Z     AssertionError:
2025-03-04T20:59:08.7966164Z     Arrays are not almost equal to 10 decimals
2025-03-04T20:59:08.7966265Z      ACTUAL: 2.3333333333333
2025-03-04T20:59:08.7966378Z      DESIRED: 2.33333334
2025-03-04T20:59:08.7966468Z     
2025-03-04T20:59:08.7966638Z     >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
2025-03-04T20:59:08.7966775Z     ...                     np.array([1.0,2.33333334]), decimal=9)
2025-03-04T20:59:08.7966936Z     Traceback (most recent call last):
2025-03-04T20:59:08.7967026Z         ...
2025-03-04T20:59:08.7967155Z     AssertionError:
2025-03-04T20:59:08.7967288Z     Arrays are not almost equal to 9 decimals
2025-03-04T20:59:08.7967398Z     <BLANKLINE>
2025-03-04T20:59:08.7967544Z     Mismatched elements: 1 / 2 (50%)
2025-03-04T20:59:08.7967700Z     Max absolute difference: 6.666699636781459e-09
2025-03-04T20:59:08.7967844Z     Max relative difference: 2.8571569790287484e-09
2025-03-04T20:59:08.7968007Z      x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-04T20:59:08.7968155Z      y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-03-04T20:59:08.7968264Z     
2025-03-04T20:59:08.7968357Z     
2025-03-04T20:59:08.7968638Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7968727Z     
2025-03-04T20:59:08.7968852Z       warnings.warn(msg)
2025-03-04T20:59:08.7968943Z     
2025-03-04T20:59:08.7969160Z --- Parse Warning: 24 / 116 ---
2025-03-04T20:59:08.7970104Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=451.
2025-03-04T20:59:08.7970397Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7970490Z     
2025-03-04T20:59:08.7970737Z     Raises an AssertionError if two items are not equal up to significant
2025-03-04T20:59:08.7970835Z     digits.
2025-03-04T20:59:08.7970939Z     
2025-03-04T20:59:08.7971152Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-03-04T20:59:08.7971356Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-04T20:59:08.7971554Z               instead of this function for more consistent floating point
2025-03-04T20:59:08.7971676Z               comparisons.
2025-03-04T20:59:08.7971767Z     
2025-03-04T20:59:08.7971978Z     Given two numbers, check that they are approximately equal.
2025-03-04T20:59:08.7972209Z     Approximately equal is defined as the number of significant digits
2025-03-04T20:59:08.7972306Z     that agree.
2025-03-04T20:59:08.7972405Z     
2025-03-04T20:59:08.7972504Z     Parameters
2025-03-04T20:59:08.7972610Z     ----------
2025-03-04T20:59:08.7972710Z     actual : scalar
2025-03-04T20:59:08.7972828Z         The object to check.
2025-03-04T20:59:08.7972927Z     desired : scalar
2025-03-04T20:59:08.7973046Z         The expected object.
2025-03-04T20:59:08.7973162Z     significant : int, optional
2025-03-04T20:59:08.7973303Z         Desired precision, default is 7.
2025-03-04T20:59:08.7973407Z     err_msg : str, optional
2025-03-04T20:59:08.7973787Z         The error message to be printed in case of failure.
2025-03-04T20:59:08.7973942Z     verbose : bool, optional
2025-03-04T20:59:08.7974366Z         If True, the conflicting values are appended to the error message.
2025-03-04T20:59:08.7974488Z     
2025-03-04T20:59:08.7974628Z     Raises
2025-03-04T20:59:08.7974755Z     ------
2025-03-04T20:59:08.7974886Z     AssertionError
2025-03-04T20:59:08.7975090Z       If actual and desired are not equal up to specified precision.
2025-03-04T20:59:08.7975190Z     
2025-03-04T20:59:08.7975285Z     See Also
2025-03-04T20:59:08.7975390Z     --------
2025-03-04T20:59:08.7975638Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:59:08.7975787Z                      relative and/or absolute precision.
2025-03-04T20:59:08.7976005Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:59:08.7976104Z     
2025-03-04T20:59:08.7976201Z     Examples
2025-03-04T20:59:08.7976294Z     --------
2025-03-04T20:59:08.7976590Z     >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)  # doctest: +SKIP
2025-03-04T20:59:08.7976852Z     >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,  # doctest: +SKIP
2025-03-04T20:59:08.7976992Z     ...                                significant=8)
2025-03-04T20:59:08.7977326Z     >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,  # doctest: +SKIP
2025-03-04T20:59:08.7977465Z     ...                                significant=8)
2025-03-04T20:59:08.7977631Z     Traceback (most recent call last):
2025-03-04T20:59:08.7977798Z         ...
2025-03-04T20:59:08.7977903Z     AssertionError:
2025-03-04T20:59:08.7978060Z     Items are not equal to 8 significant digits:
2025-03-04T20:59:08.7978167Z      ACTUAL: 1.234567e-21
2025-03-04T20:59:08.7978285Z      DESIRED: 1.2345672e-21
2025-03-04T20:59:08.7978375Z     
2025-03-04T20:59:08.7978563Z     the evaluated condition that raises the exception is
2025-03-04T20:59:08.7978653Z     
2025-03-04T20:59:08.7978849Z     >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
2025-03-04T20:59:08.7978941Z     True
2025-03-04T20:59:08.7979045Z     
2025-03-04T20:59:08.7979193Z     
2025-03-04T20:59:08.7979470Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7979561Z     
2025-03-04T20:59:08.7979679Z       warnings.warn(msg)
2025-03-04T20:59:08.7979769Z     
2025-03-04T20:59:08.7980006Z --- Parse Warning: 25 / 116 ---
2025-03-04T20:59:08.7980945Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=730.
2025-03-04T20:59:08.7981235Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7981324Z     
2025-03-04T20:59:08.7981599Z     Raises an AssertionError if two array_like objects are not equal.
2025-03-04T20:59:08.7981692Z     
2025-03-04T20:59:08.7981918Z     Given two array_like objects, check that the shape is equal and all
2025-03-04T20:59:08.7982150Z     elements of these objects are equal (but see the Notes for the special
2025-03-04T20:59:08.7982373Z     handling of a scalar). An exception is raised at shape mismatch or
2025-03-04T20:59:08.7982609Z     conflicting values. In contrast to the standard usage in numpy, NaNs
2025-03-04T20:59:08.7982852Z     are compared like numbers, no assertion is raised if both objects have
2025-03-04T20:59:08.7982969Z     NaNs in the same positions.
2025-03-04T20:59:08.7983077Z     
2025-03-04T20:59:08.7983317Z     The usual caution for verifying equality with floating point numbers is
2025-03-04T20:59:08.7983426Z     advised.
2025-03-04T20:59:08.7983517Z     
2025-03-04T20:59:08.7983628Z     Parameters
2025-03-04T20:59:08.7983725Z     ----------
2025-03-04T20:59:08.7983825Z     x : array_like
2025-03-04T20:59:08.7983952Z         The actual object to check.
2025-03-04T20:59:08.7984048Z     y : array_like
2025-03-04T20:59:08.7984179Z         The desired, expected object.
2025-03-04T20:59:08.7984314Z     err_msg : str, optional
2025-03-04T20:59:08.7984491Z         The error message to be printed in case of failure.
2025-03-04T20:59:08.7984607Z     verbose : bool, optional
2025-03-04T20:59:08.7984833Z         If True, the conflicting values are appended to the error message.
2025-03-04T20:59:08.7984941Z     strict : bool, optional
2025-03-04T20:59:08.7985163Z         If True, raise an AssertionError when either the shape or the data
2025-03-04T20:59:08.7985348Z         type of the array_like objects does not match. The special
2025-03-04T20:59:08.7985574Z         handling for scalars mentioned in the Notes section is disabled.
2025-03-04T20:59:08.7985662Z     
2025-03-04T20:59:08.7985766Z     Raises
2025-03-04T20:59:08.7985860Z     ------
2025-03-04T20:59:08.7985972Z     AssertionError
2025-03-04T20:59:08.7986119Z         If actual and desired objects are not equal.
2025-03-04T20:59:08.7986220Z     
2025-03-04T20:59:08.7986312Z     See Also
2025-03-04T20:59:08.7986418Z     --------
2025-03-04T20:59:08.7986668Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:59:08.7986820Z                      relative and/or absolute precision.
2025-03-04T20:59:08.7987067Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:59:08.7987169Z     
2025-03-04T20:59:08.7987261Z     Notes
2025-03-04T20:59:08.7987367Z     -----
2025-03-04T20:59:08.7987585Z     When one of `x` and `y` is a scalar and the other is array_like, the
2025-03-04T20:59:08.7987833Z     function checks that each element of the array_like object is equal to
2025-03-04T20:59:08.7988067Z     the scalar. This behaviour can be disabled with the `strict` parameter.
2025-03-04T20:59:08.7988169Z     
2025-03-04T20:59:08.7988262Z     Examples
2025-03-04T20:59:08.7988358Z     --------
2025-03-04T20:59:08.7988514Z     The first assert does not raise an exception:
2025-03-04T20:59:08.7988602Z     
2025-03-04T20:59:08.7988776Z     >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
2025-03-04T20:59:08.7988912Z     ...                               [np.exp(0),2.33333, np.nan])
2025-03-04T20:59:08.7989011Z     
2025-03-04T20:59:08.7989247Z     Use `assert_allclose` or one of the nulp (number of floating point values)
2025-03-04T20:59:08.7989382Z     functions for these cases instead:
2025-03-04T20:59:08.7989471Z     
2025-03-04T20:59:08.7989639Z     >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
2025-03-04T20:59:08.7989775Z     ...                            [1, np.sqrt(np.pi)**2, np.nan],
2025-03-04T20:59:08.7989907Z     ...                            rtol=1e-10, atol=0)
2025-03-04T20:59:08.7989998Z     
2025-03-04T20:59:08.7990227Z     As mentioned in the Notes section, `assert_array_equal` has special
2025-03-04T20:59:08.7990487Z     handling for scalars. Here the test checks that each value in `x` is 3:
2025-03-04T20:59:08.7990590Z     
2025-03-04T20:59:08.7990708Z     >>> x = np.full((2, 5), fill_value=3)
2025-03-04T20:59:08.7990848Z     >>> np.testing.assert_array_equal(x, 3)
2025-03-04T20:59:08.7990943Z     
2025-03-04T20:59:08.7991178Z     Use `strict` to raise an AssertionError when comparing a scalar with an
2025-03-04T20:59:08.7991276Z     array:
2025-03-04T20:59:08.7991379Z     
2025-03-04T20:59:08.7991538Z     >>> np.testing.assert_array_equal(x, 3, strict=True)
2025-03-04T20:59:08.7991676Z     Traceback (most recent call last):
2025-03-04T20:59:08.7991769Z         ...
2025-03-04T20:59:08.7991885Z     AssertionError:
2025-03-04T20:59:08.7991990Z     Arrays are not equal
2025-03-04T20:59:08.7992100Z     <BLANKLINE>
2025-03-04T20:59:08.7992209Z     (shapes (2, 5), () mismatch)
2025-03-04T20:59:08.7992337Z      x: torch.ndarray([[3, 3, 3, 3, 3],
2025-03-04T20:59:08.7992437Z             [3, 3, 3, 3, 3]])
2025-03-04T20:59:08.7992541Z      y: torch.ndarray(3)
2025-03-04T20:59:08.7992645Z     
2025-03-04T20:59:08.7992867Z     The `strict` parameter also ensures that the array data types match:
2025-03-04T20:59:08.7992967Z     
2025-03-04T20:59:08.7993074Z     >>> x = np.array([2, 2, 2])
2025-03-04T20:59:08.7993255Z     >>> y = np.array([2., 2., 2.], dtype=np.float32)
2025-03-04T20:59:08.7993412Z     >>> np.testing.assert_array_equal(x, y, strict=True)
2025-03-04T20:59:08.7993549Z     Traceback (most recent call last):
2025-03-04T20:59:08.7993640Z         ...
2025-03-04T20:59:08.7993752Z     AssertionError:
2025-03-04T20:59:08.7993856Z     Arrays are not equal
2025-03-04T20:59:08.7993966Z     <BLANKLINE>
2025-03-04T20:59:08.7994121Z     (dtypes dtype("int64"), dtype("float32") mismatch)
2025-03-04T20:59:08.7994247Z      x: torch.ndarray([2, 2, 2])
2025-03-04T20:59:08.7994358Z      y: torch.ndarray([2., 2., 2.])
2025-03-04T20:59:08.7994462Z     
2025-03-04T20:59:08.7994726Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.7994832Z     
2025-03-04T20:59:08.7994939Z       warnings.warn(msg)
2025-03-04T20:59:08.7995042Z     
2025-03-04T20:59:08.7995254Z --- Parse Warning: 26 / 116 ---
2025-03-04T20:59:08.7996237Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=836.
2025-03-04T20:59:08.7996546Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.7996652Z     
2025-03-04T20:59:08.7996904Z     Raises an AssertionError if two objects are not equal up to desired
2025-03-04T20:59:08.7997016Z     precision.
2025-03-04T20:59:08.7997106Z     
2025-03-04T20:59:08.7997310Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-03-04T20:59:08.7997502Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-03-04T20:59:08.7997717Z               instead of this function for more consistent floating point
2025-03-04T20:59:08.7997825Z               comparisons.
2025-03-04T20:59:08.7997930Z     
2025-03-04T20:59:08.7998184Z     The test verifies identical shapes and that the elements of ``actual`` and
2025-03-04T20:59:08.7998304Z     ``desired`` satisfy.
2025-03-04T20:59:08.7998397Z     
2025-03-04T20:59:08.7998555Z         ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2025-03-04T20:59:08.7998645Z     
2025-03-04T20:59:08.7998897Z     That is a looser test than originally documented, but agrees with what the
2025-03-04T20:59:08.7999153Z     actual implementation did up to rounding vagaries. An exception is raised
2025-03-04T20:59:08.7999412Z     at shape mismatch or conflicting values. In contrast to the standard usage
2025-03-04T20:59:08.7999640Z     in numpy, NaNs are compared like numbers, no assertion is raised if both
2025-03-04T20:59:08.7999788Z     objects have NaNs in the same positions.
2025-03-04T20:59:08.7999878Z     
2025-03-04T20:59:08.8000003Z     Parameters
2025-03-04T20:59:08.8000110Z     ----------
2025-03-04T20:59:08.8000207Z     x : array_like
2025-03-04T20:59:08.8000336Z         The actual object to check.
2025-03-04T20:59:08.8000432Z     y : array_like
2025-03-04T20:59:08.8000565Z         The desired, expected object.
2025-03-04T20:59:08.8000673Z     decimal : int, optional
2025-03-04T20:59:08.8000810Z         Desired precision, default is 6.
2025-03-04T20:59:08.8000920Z     err_msg : str, optional
2025-03-04T20:59:08.8001095Z       The error message to be printed in case of failure.
2025-03-04T20:59:08.8001204Z     verbose : bool, optional
2025-03-04T20:59:08.8001432Z         If True, the conflicting values are appended to the error message.
2025-03-04T20:59:08.8001521Z     
2025-03-04T20:59:08.8001622Z     Raises
2025-03-04T20:59:08.8001714Z     ------
2025-03-04T20:59:08.8001826Z     AssertionError
2025-03-04T20:59:08.8002031Z         If actual and desired are not equal up to specified precision.
2025-03-04T20:59:08.8002131Z     
2025-03-04T20:59:08.8002226Z     See Also
2025-03-04T20:59:08.8002331Z     --------
2025-03-04T20:59:08.8002580Z     assert_allclose: Compare two array_like objects for equality with desired
2025-03-04T20:59:08.8002729Z                      relative and/or absolute precision.
2025-03-04T20:59:08.8002973Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-03-04T20:59:08.8003076Z     
2025-03-04T20:59:08.8003169Z     Examples
2025-03-04T20:59:08.8003273Z     --------
2025-03-04T20:59:08.8003414Z     the first assert does not raise an exception
2025-03-04T20:59:08.8003503Z     
2025-03-04T20:59:08.8003700Z     >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
2025-03-04T20:59:08.8003829Z     ...                                      [1.0,2.333,np.nan])
2025-03-04T20:59:08.8003932Z     
2025-03-04T20:59:08.8004119Z     >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-04T20:59:08.8004268Z     ...                                      [1.0,2.33339,np.nan], decimal=5)
2025-03-04T20:59:08.8004392Z     Traceback (most recent call last):
2025-03-04T20:59:08.8004498Z         ...
2025-03-04T20:59:08.8004601Z     AssertionError:
2025-03-04T20:59:08.8004746Z     Arrays are not almost equal to 5 decimals
2025-03-04T20:59:08.8004843Z     <BLANKLINE>
2025-03-04T20:59:08.8004974Z     Mismatched elements: 1 / 3 (33.3%)
2025-03-04T20:59:08.8005144Z     Max absolute difference: 5.999999999994898e-05
2025-03-04T20:59:08.8005296Z     Max relative difference: 2.5713661239633743e-05
2025-03-04T20:59:08.8005467Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-04T20:59:08.8005671Z      y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2025-03-04T20:59:08.8005762Z     
2025-03-04T20:59:08.8005959Z     >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2025-03-04T20:59:08.8006088Z     ...                                      [1.0,2.33333, 5], decimal=5)
2025-03-04T20:59:08.8006223Z     Traceback (most recent call last):
2025-03-04T20:59:08.8006314Z         ...
2025-03-04T20:59:08.8006430Z     AssertionError:
2025-03-04T20:59:08.8006560Z     Arrays are not almost equal to 5 decimals
2025-03-04T20:59:08.8006666Z     <BLANKLINE>
2025-03-04T20:59:08.8006781Z     x and y nan location mismatch:
2025-03-04T20:59:08.8006963Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-03-04T20:59:08.8007128Z      y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2025-03-04T20:59:08.8007231Z     
2025-03-04T20:59:08.8007319Z     
2025-03-04T20:59:08.8007595Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8007687Z     
2025-03-04T20:59:08.8007806Z       warnings.warn(msg)
2025-03-04T20:59:08.8007897Z     
2025-03-04T20:59:08.8008104Z --- Parse Warning: 27 / 116 ---
2025-03-04T20:59:08.8009106Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1786.
2025-03-04T20:59:08.8009396Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8009620Z     Context manager that resets warning registry for catching warnings
2025-03-04T20:59:08.8009726Z     
2025-03-04T20:59:08.8009982Z         Warnings can be slippery, because, whenever a warning is triggered, Python
2025-03-04T20:59:08.8010235Z         adds a ``__warningregistry__`` member to the *calling* module.  This makes
2025-03-04T20:59:08.8010485Z         it impossible to retrigger the warning in this module, whatever you put in
2025-03-04T20:59:08.8010748Z         the warnings filters.  This context manager accepts a sequence of `modules`
2025-03-04T20:59:08.8010900Z         as a keyword argument to its constructor and:
2025-03-04T20:59:08.8011001Z     
2025-03-04T20:59:08.8011238Z         * stores and removes any ``__warningregistry__`` entries in given `modules`
2025-03-04T20:59:08.8011347Z           on entry;
2025-03-04T20:59:08.8011546Z         * resets ``__warningregistry__`` to its previous state on exit.
2025-03-04T20:59:08.8011637Z     
2025-03-04T20:59:08.8011881Z         This makes it possible to trigger any warning afresh inside the context
2025-03-04T20:59:08.8012117Z         manager without disturbing the state of warnings outside.
2025-03-04T20:59:08.8012221Z     
2025-03-04T20:59:08.8012465Z         For compatibility with Python 3.0, please consider all arguments to be
2025-03-04T20:59:08.8012581Z         keyword-only.
2025-03-04T20:59:08.8012671Z     
2025-03-04T20:59:08.8012782Z         Parameters
2025-03-04T20:59:08.8012881Z         ----------
2025-03-04T20:59:08.8013004Z         record : bool, optional
2025-03-04T20:59:08.8013200Z             Specifies whether warnings should be captured by a custom
2025-03-04T20:59:08.8013459Z             implementation of ``warnings.showwarning()`` and be appended to a list
2025-03-04T20:59:08.8013681Z             returned by the context manager. Otherwise None is returned by the
2025-03-04T20:59:08.8013929Z             context manager. The objects appended to the list are arguments whose
2025-03-04T20:59:08.8014109Z             attributes mirror the arguments to ``showwarning()``.
2025-03-04T20:59:08.8014238Z         modules : sequence, optional
2025-03-04T20:59:08.8014468Z             Sequence of modules for which to reset warnings registry on entry and
2025-03-04T20:59:08.8014707Z             restore on exit. To work correctly, all 'ignore' filters should
2025-03-04T20:59:08.8014830Z             filter by one of these modules.
2025-03-04T20:59:08.8014934Z     
2025-03-04T20:59:08.8015057Z         Examples
2025-03-04T20:59:08.8015169Z         --------
2025-03-04T20:59:08.8015274Z         >>> import warnings
2025-03-04T20:59:08.8015477Z         >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2025-03-04T20:59:08.8015614Z         ...         modules=[np.core.fromnumeric]):
2025-03-04T20:59:08.8015758Z         ...     warnings.simplefilter('always')
2025-03-04T20:59:08.8015995Z         ...     warnings.filterwarnings('ignore', module='np.core.fromnumeric')
2025-03-04T20:59:08.8016185Z         ...     # do something that raises a warning but ignore those in
2025-03-04T20:59:08.8016301Z         ...     # np.core.fromnumeric
2025-03-04T20:59:08.8016402Z         
2025-03-04T20:59:08.8016664Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8016767Z     
2025-03-04T20:59:08.8016872Z       warnings.warn(msg)
2025-03-04T20:59:08.8016973Z     
2025-03-04T20:59:08.8017170Z --- Parse Warning: 28 / 116 ---
2025-03-04T20:59:08.8018186Z     /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.
2025-03-04T20:59:08.8018461Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8018734Z     Applies a 1D convolution over a quantized input signal composed of
2025-03-04T20:59:08.8018860Z         several quantized input planes.
2025-03-04T20:59:08.8018961Z     
2025-03-04T20:59:08.8019182Z         For details on input arguments, parameters, and implementation see
2025-03-04T20:59:08.8019313Z         :class:`~torch.nn.Conv1d`.
2025-03-04T20:59:08.8019405Z     
2025-03-04T20:59:08.8019518Z         .. note::
2025-03-04T20:59:08.8019726Z             Only `zeros` is supported for the :attr:`padding_mode` argument.
2025-03-04T20:59:08.8019830Z     
2025-03-04T20:59:08.8019926Z         .. note::
2025-03-04T20:59:08.8020131Z             Only `torch.quint8` is supported for the input data type.
2025-03-04T20:59:08.8020221Z     
2025-03-04T20:59:08.8020309Z     
2025-03-04T20:59:08.8020424Z         Attributes:
2025-03-04T20:59:08.8020646Z             weight (Tensor):     packed tensor derived from the learnable weight
2025-03-04T20:59:08.8020771Z                                  parameter.
2025-03-04T20:59:08.8020929Z             scale (Tensor):      scalar for the output scale
2025-03-04T20:59:08.8021117Z             zero_point (Tensor): scalar for the output zero point
2025-03-04T20:59:08.8021210Z     
2025-03-04T20:59:08.8021411Z         See :class:`~torch.nn.Conv1d` for other attributes.
2025-03-04T20:59:08.8021502Z     
2025-03-04T20:59:08.8021612Z         Examples::
2025-03-04T20:59:08.8021704Z     
2025-03-04T20:59:08.8021874Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2025-03-04T20:59:08.8022017Z             >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2025-03-04T20:59:08.8022156Z             >>> input = torch.randn(20, 16, 100)
2025-03-04T20:59:08.8022276Z             >>> # quantize input to quint8
2025-03-04T20:59:08.8022396Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8022620Z             >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2025-03-04T20:59:08.8022769Z             ...                                     dtype=torch.quint8)
2025-03-04T20:59:08.8022882Z             >>> output = m(q_input)
2025-03-04T20:59:08.8022992Z     
2025-03-04T20:59:08.8023084Z         
2025-03-04T20:59:08.8023359Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8023452Z     
2025-03-04T20:59:08.8023573Z       warnings.warn(msg)
2025-03-04T20:59:08.8023664Z     
2025-03-04T20:59:08.8023913Z --- Parse Warning: 29 / 116 ---
2025-03-04T20:59:08.8024843Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11.
2025-03-04T20:59:08.8025133Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8025275Z     A quantized long short-term memory (LSTM).
2025-03-04T20:59:08.8025384Z     
2025-03-04T20:59:08.8025677Z         For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2025-03-04T20:59:08.8025783Z     
2025-03-04T20:59:08.8025884Z         Attributes:
2025-03-04T20:59:08.8026032Z             layers : instances of the `_LSTMLayer`
2025-03-04T20:59:08.8026124Z     
2025-03-04T20:59:08.8026238Z         .. note::
2025-03-04T20:59:08.8026464Z             To access the weights and biases, you need to access them per layer.
2025-03-04T20:59:08.8026666Z             See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2025-03-04T20:59:08.8026758Z     
2025-03-04T20:59:08.8026871Z         Examples::
2025-03-04T20:59:08.8026984Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8027105Z             >>> custom_module_config = {
2025-03-04T20:59:08.8027273Z             ...     'float_to_observed_custom_module_class': {
2025-03-04T20:59:08.8027414Z             ...         nn.LSTM: nn.quantizable.LSTM,
2025-03-04T20:59:08.8027526Z             ...     },
2025-03-04T20:59:08.8027689Z             ...     'observed_to_quantized_custom_module_class': {
2025-03-04T20:59:08.8027884Z             ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2025-03-04T20:59:08.8027978Z             ...     }
2025-03-04T20:59:08.8028083Z             ... }
2025-03-04T20:59:08.8028314Z             >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2025-03-04T20:59:08.8028549Z             >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2025-03-04T20:59:08.8028643Z         
2025-03-04T20:59:08.8028918Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8029007Z     
2025-03-04T20:59:08.8029127Z       warnings.warn(msg)
2025-03-04T20:59:08.8029217Z     
2025-03-04T20:59:08.8029425Z --- Parse Warning: 30 / 116 ---
2025-03-04T20:59:08.8030492Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227.
2025-03-04T20:59:08.8030773Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8030961Z     Squashes the sparse masks into the appropriate tensors.
2025-03-04T20:59:08.8031090Z     
2025-03-04T20:59:08.8031307Z             If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2025-03-04T20:59:08.8031515Z             the module will have a `sparse_params` dict attached to it.
2025-03-04T20:59:08.8031603Z     
2025-03-04T20:59:08.8031708Z             Args:
2025-03-04T20:59:08.8031904Z                 params_to_keep: List of keys to save in the module or a dict
2025-03-04T20:59:08.8032085Z                                 representing the modules and keys that will have
2025-03-04T20:59:08.8032220Z                                 sparsity parameters saved
2025-03-04T20:59:08.8032454Z                 params_to_keep_per_layer: Dict to specify the params that should be
2025-03-04T20:59:08.8032620Z                                 saved for specific layers. The keys in the dict
2025-03-04T20:59:08.8032801Z                                 should be the module fqn, while the values should
2025-03-04T20:59:08.8032969Z                                 be a list of strings with the names of the variables
2025-03-04T20:59:08.8033119Z                                 to save in the `sparse_params`
2025-03-04T20:59:08.8033238Z     
2025-03-04T20:59:08.8033349Z             Examples:
2025-03-04T20:59:08.8033494Z                 >>> # xdoctest: +SKIP("locals are undefined")
2025-03-04T20:59:08.8033633Z                 >>> # Don't save any sparse params
2025-03-04T20:59:08.8033786Z                 >>> sparsifier.squash_mask()
2025-03-04T20:59:08.8033952Z                 >>> hasattr(model.submodule1, 'sparse_params')
2025-03-04T20:59:08.8034049Z                 False
2025-03-04T20:59:08.8034150Z     
2025-03-04T20:59:08.8034283Z                 >>> # Keep sparse params per layer
2025-03-04T20:59:08.8034423Z                 >>> sparsifier.squash_mask(
2025-03-04T20:59:08.8034550Z                 ...     params_to_keep_per_layer={
2025-03-04T20:59:08.8034712Z                 ...         'submodule1.linear1': ('foo', 'bar'),
2025-03-04T20:59:08.8034848Z                 ...         'submodule2.linear42': ('baz',)
2025-03-04T20:59:08.8034958Z                 ...     })
2025-03-04T20:59:08.8035126Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-03-04T20:59:08.8035250Z                 {'foo': 42, 'bar': 24}
2025-03-04T20:59:08.8035421Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-03-04T20:59:08.8035536Z                 {'baz': 0.1}
2025-03-04T20:59:08.8035625Z     
2025-03-04T20:59:08.8035770Z                 >>> # Keep sparse params for all layers
2025-03-04T20:59:08.8035957Z                 >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
2025-03-04T20:59:08.8036134Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-03-04T20:59:08.8036245Z                 {'foo': 42, 'bar': 24}
2025-03-04T20:59:08.8036449Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-03-04T20:59:08.8036557Z                 {'foo': 42, 'bar': 24}
2025-03-04T20:59:08.8036658Z     
2025-03-04T20:59:08.8036867Z                 >>> # Keep some sparse params for all layers, and specific ones for
2025-03-04T20:59:08.8036990Z                 >>> # some other layers
2025-03-04T20:59:08.8037114Z                 >>> sparsifier.squash_mask(
2025-03-04T20:59:08.8037253Z                 ...     params_to_keep=('foo', 'bar'),
2025-03-04T20:59:08.8037378Z                 ...     params_to_keep_per_layer={
2025-03-04T20:59:08.8037517Z                 ...         'submodule2.linear42': ('baz',)
2025-03-04T20:59:08.8037628Z                 ...     })
2025-03-04T20:59:08.8037794Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-03-04T20:59:08.8037914Z                 {'foo': 42, 'bar': 24}
2025-03-04T20:59:08.8038081Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-03-04T20:59:08.8038216Z                 {'foo': 42, 'bar': 24, 'baz': 0.1}
2025-03-04T20:59:08.8038309Z             
2025-03-04T20:59:08.8038584Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8038703Z     
2025-03-04T20:59:08.8038821Z       warnings.warn(msg)
2025-03-04T20:59:08.8038912Z     
2025-03-04T20:59:08.8039124Z --- Parse Warning: 31 / 116 ---
2025-03-04T20:59:08.8040169Z     /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.
2025-03-04T20:59:08.8040455Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8040543Z     
2025-03-04T20:59:08.8040816Z     Config object that specifies the supported data types passed as arguments to
2025-03-04T20:59:08.8041065Z     quantize ops in the reference model spec, for input and output activations,
2025-03-04T20:59:08.8041180Z     weights, and biases.
2025-03-04T20:59:08.8041270Z     
2025-03-04T20:59:08.8041452Z     For example, consider the following reference model:
2025-03-04T20:59:08.8041544Z     
2025-03-04T20:59:08.8041725Z       quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2025-03-04T20:59:08.8041844Z     
2025-03-04T20:59:08.8042079Z     The pattern in the square brackets refers to the reference pattern of
2025-03-04T20:59:08.8042325Z     statically quantized linear. Setting the input dtype as `torch.quint8`
2025-03-04T20:59:08.8042601Z     in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2025-03-04T20:59:08.8042836Z     to the first quantize op (quant1). Similarly, setting the output dtype as
2025-03-04T20:59:08.8043075Z     `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2025-03-04T20:59:08.8043195Z     the second quantize op (quant2).
2025-03-04T20:59:08.8043297Z     
2025-03-04T20:59:08.8043525Z     Note that the dtype here does not refer to the interface dtypes of the
2025-03-04T20:59:08.8043752Z     op. For example, the "input dtype" here is not the dtype of the input
2025-03-04T20:59:08.8043975Z     tensor passed to the quantized linear op. Though it can still be the
2025-03-04T20:59:08.8044194Z     same as the interface dtype, this is not always the case, e.g. the
2025-03-04T20:59:08.8044421Z     interface dtype is fp32 in dynamic quantization but the "input dtype"
2025-03-04T20:59:08.8044655Z     specified in the DTypeConfig would still be quint8. The semantics of
2025-03-04T20:59:08.8044873Z     dtypes here are the same as the semantics of the dtypes specified in
2025-03-04T20:59:08.8044986Z     the observers.
2025-03-04T20:59:08.8045074Z     
2025-03-04T20:59:08.8045302Z     These dtypes are matched against the ones specified in the user's
2025-03-04T20:59:08.8045527Z     QConfig. If there is a match, and the QConfig satisfies the constraints
2025-03-04T20:59:08.8045812Z     specified in the DTypeConfig (if any), then we will quantize the given
2025-03-04T20:59:08.8046050Z     pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2025-03-04T20:59:08.8046182Z     the pattern will not be quantized.
2025-03-04T20:59:08.8046274Z     
2025-03-04T20:59:08.8046392Z     Example usage::
2025-03-04T20:59:08.8046484Z     
2025-03-04T20:59:08.8046613Z         >>> # xdoctest: +SKIP(failing)
2025-03-04T20:59:08.8046732Z         >>> dtype_config1 = DTypeConfig(
2025-03-04T20:59:08.8046863Z         ...     input_dtype=torch.quint8,
2025-03-04T20:59:08.8046983Z         ...     output_dtype=torch.quint8,
2025-03-04T20:59:08.8047112Z         ...     weight_dtype=torch.qint8,
2025-03-04T20:59:08.8047226Z         ...     bias_dtype=torch.float)
2025-03-04T20:59:08.8047326Z     
2025-03-04T20:59:08.8047443Z         >>> dtype_config2 = DTypeConfig(
2025-03-04T20:59:08.8047581Z         ...     input_dtype=DTypeWithConstraints(
2025-03-04T20:59:08.8047703Z         ...         dtype=torch.quint8,
2025-03-04T20:59:08.8047823Z         ...         quant_min_lower_bound=0,
2025-03-04T20:59:08.8047957Z         ...         quant_max_upper_bound=255,
2025-03-04T20:59:08.8048049Z         ...     ),
2025-03-04T20:59:08.8048228Z         ...     output_dtype=DTypeWithConstraints(
2025-03-04T20:59:08.8048339Z         ...         dtype=torch.quint8,
2025-03-04T20:59:08.8048471Z         ...         quant_min_lower_bound=0,
2025-03-04T20:59:08.8048597Z         ...         quant_max_upper_bound=255,
2025-03-04T20:59:08.8048706Z         ...     ),
2025-03-04T20:59:08.8048846Z         ...     weight_dtype=DTypeWithConstraints(
2025-03-04T20:59:08.8048970Z         ...         dtype=torch.qint8,
2025-03-04T20:59:08.8049096Z         ...         quant_min_lower_bound=-128,
2025-03-04T20:59:08.8049232Z         ...         quant_max_upper_bound=127,
2025-03-04T20:59:08.8049323Z         ...     ),
2025-03-04T20:59:08.8049450Z         ...     bias_dtype=torch.float)
2025-03-04T20:59:08.8049540Z     
2025-03-04T20:59:08.8049673Z         >>> dtype_config1.input_dtype
2025-03-04T20:59:08.8049772Z         torch.quint8
2025-03-04T20:59:08.8049872Z     
2025-03-04T20:59:08.8073362Z         >>> dtype_config2.input_dtype
2025-03-04T20:59:08.8073572Z         torch.quint8
2025-03-04T20:59:08.8073912Z     
2025-03-04T20:59:08.8074086Z         >>> dtype_config2.input_dtype_with_constraints
2025-03-04T20:59:08.8074804Z         DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2025-03-04T20:59:08.8074893Z     
2025-03-04T20:59:08.8075221Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8075307Z     
2025-03-04T20:59:08.8075413Z       warnings.warn(msg)
2025-03-04T20:59:08.8075505Z     
2025-03-04T20:59:08.8075764Z --- Parse Warning: 32 / 116 ---
2025-03-04T20:59:08.8077043Z     /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.
2025-03-04T20:59:08.8077327Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8077414Z     
2025-03-04T20:59:08.8077705Z     Takes in optional filter values and generates two tables with desired information.
2025-03-04T20:59:08.8077790Z     
2025-03-04T20:59:08.8078006Z     The generated tables are presented in both a list-of-lists format
2025-03-04T20:59:08.8078103Z     
2025-03-04T20:59:08.8078316Z     The reason for the two tables are that they handle different things:
2025-03-04T20:59:08.8078495Z     1.) the first table handles all tensor level information
2025-03-04T20:59:08.8078719Z     2.) the second table handles and displays all channel based information
2025-03-04T20:59:08.8078813Z     
2025-03-04T20:59:08.8079180Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-04T20:59:08.8079526Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-04T20:59:08.8079892Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-04T20:59:08.8079991Z     
2025-03-04T20:59:08.8080098Z     Tensor table columns:
2025-03-04T20:59:08.8080307Z         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:59:08.8080473Z         ----  ---------  ---------   ---------   ---------        ---------
2025-03-04T20:59:08.8080571Z     
2025-03-04T20:59:08.8080685Z     Per-Channel table columns:
2025-03-04T20:59:08.8080924Z         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:59:08.8081099Z         ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-04T20:59:08.8081195Z     
2025-03-04T20:59:08.8081288Z     Args:
2025-03-04T20:59:08.8081570Z         feature_filter (str, optional): Filters the features presented to only those that
2025-03-04T20:59:08.8081686Z             contain this filter substring
2025-03-04T20:59:08.8081912Z             Default = "", results in all the features being printed
2025-03-04T20:59:08.8082176Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:59:08.8082440Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:59:08.8082523Z     
2025-03-04T20:59:08.8082656Z     Returns a dictionary with two keys:
2025-03-04T20:59:08.8082837Z         (Dict[str, Tuple[List, List]]) A dict containing two keys:
2025-03-04T20:59:08.8082979Z         "tensor_level_info", "channel_level_info"
2025-03-04T20:59:08.8083096Z             Each key maps to a tuple with:
2025-03-04T20:59:08.8083233Z                 A list of the headers of each table
2025-03-04T20:59:08.8083422Z                 A list of lists containing the table information row by row
2025-03-04T20:59:08.8083611Z                 The 0th index row will contain the headers of the columns
2025-03-04T20:59:08.8083742Z                 The rest of the rows will contain data
2025-03-04T20:59:08.8083840Z     
2025-03-04T20:59:08.8083933Z     Example Use:
2025-03-04T20:59:08.8084078Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8084262Z         >>> mod_report_visualizer.generate_filtered_tables(
2025-03-04T20:59:08.8084401Z         ...     feature_filter = "per_channel_min",
2025-03-04T20:59:08.8084538Z         ...     module_fqn_filter = "block1"
2025-03-04T20:59:08.8084831Z         ... ) # generates table with per_channel_min info for all modules in block 1 of the model
2025-03-04T20:59:08.8084915Z     
2025-03-04T20:59:08.8085185Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8085267Z     
2025-03-04T20:59:08.8085377Z       warnings.warn(msg)
2025-03-04T20:59:08.8085462Z     
2025-03-04T20:59:08.8085672Z --- Parse Warning: 33 / 116 ---
2025-03-04T20:59:08.8086951Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400.
2025-03-04T20:59:08.8087236Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8087321Z     
2025-03-04T20:59:08.8087609Z     Takes in optional filter values and prints out formatted tables of the information.
2025-03-04T20:59:08.8087694Z     
2025-03-04T20:59:08.8088053Z     The reason for the two tables printed out instead of one large one are that they handle different things:
2025-03-04T20:59:08.8088221Z     1.) the first table handles all tensor level information
2025-03-04T20:59:08.8088496Z     2.) the second table handles and displays all channel based information
2025-03-04T20:59:08.8088580Z     
2025-03-04T20:59:08.8088915Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-03-04T20:59:08.8089249Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-03-04T20:59:08.8089619Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-03-04T20:59:08.8089702Z     
2025-03-04T20:59:08.8089814Z     Tensor table columns:
2025-03-04T20:59:08.8090013Z      idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:59:08.8090182Z     ----  ---------  ---------   ---------   ---------        ---------
2025-03-04T20:59:08.8090263Z     
2025-03-04T20:59:08.8090388Z     Per-Channel table columns:
2025-03-04T20:59:08.8090470Z     
2025-03-04T20:59:08.8090702Z      idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-03-04T20:59:08.8090868Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2025-03-04T20:59:08.8090958Z     
2025-03-04T20:59:08.8091045Z     Args:
2025-03-04T20:59:08.8091324Z         feature_filter (str, optional): Filters the features presented to only those that
2025-03-04T20:59:08.8091466Z             contain this filter substring
2025-03-04T20:59:08.8091649Z             Default = "", results in all the features being printed
2025-03-04T20:59:08.8091915Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:59:08.8092179Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:59:08.8092265Z     
2025-03-04T20:59:08.8092369Z     Example Use:
2025-03-04T20:59:08.8092504Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8092676Z         >>> mod_report_visualizer.generate_table_visualization(
2025-03-04T20:59:08.8092818Z         ...     feature_filter = "per_channel_min",
2025-03-04T20:59:08.8092933Z         ...     module_fqn_filter = "block1"
2025-03-04T20:59:08.8093031Z         ... )
2025-03-04T20:59:08.8093228Z         >>> # prints out neatly formatted table with per_channel_min info
2025-03-04T20:59:08.8093370Z         >>> # for all modules in block 1 of the model
2025-03-04T20:59:08.8093456Z     
2025-03-04T20:59:08.8093748Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8093832Z     
2025-03-04T20:59:08.8093942Z       warnings.warn(msg)
2025-03-04T20:59:08.8094025Z     
2025-03-04T20:59:08.8094254Z --- Parse Warning: 34 / 116 ---
2025-03-04T20:59:08.8095516Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566.
2025-03-04T20:59:08.8095794Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8095878Z     
2025-03-04T20:59:08.8096134Z     Takes in a feature and optional module_filter and plots of the desired data.
2025-03-04T20:59:08.8096220Z     
2025-03-04T20:59:08.8096511Z     For per channel features, it averages the value across the channels and plots a point
2025-03-04T20:59:08.8096771Z     per module. The reason for this is that for models with hundreds of channels, it can
2025-03-04T20:59:08.8097063Z     be hard to differentiate one channel line from another, and so the point of generating
2025-03-04T20:59:08.8097338Z     a single average point per module is to give a sense of general trends that encourage
2025-03-04T20:59:08.8097451Z     further deep dives.
2025-03-04T20:59:08.8097534Z     
2025-03-04T20:59:08.8097634Z     Note:
2025-03-04T20:59:08.8097986Z         Only features in the report that have tensor value data are plottable by this class
2025-03-04T20:59:08.8098203Z         When the tensor information is plotted, it will plot:
2025-03-04T20:59:08.8098345Z             idx as the x val, feature value as the y_val
2025-03-04T20:59:08.8098529Z         When the channel information is plotted, it will plot:
2025-03-04T20:59:08.8098801Z             the first idx of each module as the x val, feature value as the y_val [for each channel]
2025-03-04T20:59:08.8099048Z             The reason for this is that we want to be able to compare values across the
2025-03-04T20:59:08.8099291Z             channels for same layer, and it will be hard if values are staggered by idx
2025-03-04T20:59:08.8099479Z             This means each module is represented by only 1 x value
2025-03-04T20:59:08.8099572Z     Args:
2025-03-04T20:59:08.8099817Z         feature_filter (str): Filters the features presented to only those that
2025-03-04T20:59:08.8099930Z             contain this filter substring
2025-03-04T20:59:08.8100208Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:59:08.8100461Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:59:08.8100560Z     
2025-03-04T20:59:08.8100656Z     Example Use:
2025-03-04T20:59:08.8100825Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8100991Z         >>> mod_report_visualizer.generate_plot_visualization(
2025-03-04T20:59:08.8101135Z         ...     feature_filter = "per_channel_min",
2025-03-04T20:59:08.8101250Z         ...     module_fqn_filter = "block1"
2025-03-04T20:59:08.8101351Z         ... )
2025-03-04T20:59:08.8101539Z         >>> # outputs line plot of per_channel_min information for all
2025-03-04T20:59:08.8101742Z         >>> # modules in block1 of model each channel gets it's own line,
2025-03-04T20:59:08.8101919Z         >>> # and it's plotted across the in-order modules on the x-axis
2025-03-04T20:59:08.8102016Z     
2025-03-04T20:59:08.8102274Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8102372Z     
2025-03-04T20:59:08.8102471Z       warnings.warn(msg)
2025-03-04T20:59:08.8102564Z     
2025-03-04T20:59:08.8102761Z --- Parse Warning: 35 / 116 ---
2025-03-04T20:59:08.8104068Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646.
2025-03-04T20:59:08.8104391Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8104487Z     
2025-03-04T20:59:08.8104772Z     Takes in a feature and optional module_filter and plots the histogram of desired data.
2025-03-04T20:59:08.8104868Z     
2025-03-04T20:59:08.8104960Z     Note:
2025-03-04T20:59:08.8105238Z         Only features in the report that have tensor value data can be viewed as a histogram
2025-03-04T20:59:08.8105509Z         If you want to plot a histogram from all the channel values of a specific feature for
2025-03-04T20:59:08.8105767Z             a specific model, make sure to specify both the model and the feature properly
2025-03-04T20:59:08.8106019Z             in the filters and you should be able to see a distribution of the channel data
2025-03-04T20:59:08.8106112Z     
2025-03-04T20:59:08.8106200Z     Args:
2025-03-04T20:59:08.8106478Z         feature_filter (str, optional): Filters the features presented to only those that
2025-03-04T20:59:08.8106597Z             contain this filter substring
2025-03-04T20:59:08.8106774Z             Default = "", results in all the features being printed
2025-03-04T20:59:08.8107042Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-03-04T20:59:08.8107306Z             Default = "", results in all the modules in the reports to be visible in the table
2025-03-04T20:59:08.8107563Z         num_bins (int, optional): The number of bins to create the histogram with
2025-03-04T20:59:08.8107771Z             Default = 10, the values will be split into 10 equal sized bins
2025-03-04T20:59:08.8107859Z     
2025-03-04T20:59:08.8107968Z     Example Use:
2025-03-04T20:59:08.8108077Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8108400Z         >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2025-03-04T20:59:08.8108536Z         ...     feature_filter = "per_channel_min",
2025-03-04T20:59:08.8108667Z         ...     module_fqn_filter = "block1"
2025-03-04T20:59:08.8108760Z         ... )
2025-03-04T20:59:08.8109058Z         # outputs histogram of per_channel_min information for all modules in block1 of model
2025-03-04T20:59:08.8109326Z             information is gathered across all channels for all modules in block 1 for the
2025-03-04T20:59:08.8109573Z             per_channel_min and is displayed in a histogram of equally sized bins
2025-03-04T20:59:08.8109662Z     
2025-03-04T20:59:08.8109941Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8110032Z     
2025-03-04T20:59:08.8110153Z       warnings.warn(msg)
2025-03-04T20:59:08.8110242Z     
2025-03-04T20:59:08.8110478Z --- Parse Warning: 36 / 116 ---
2025-03-04T20:59:08.8111441Z     /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=666.
2025-03-04T20:59:08.8111717Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-03-04T20:59:08.8111807Z     
2025-03-04T20:59:08.8112156Z     Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2025-03-04T20:59:08.8112524Z     The submesh created consists of the dimensions and the communicators indicated by
2025-03-04T20:59:08.8112639Z     ``mesh_dim_names``
2025-03-04T20:59:08.8112771Z     
2025-03-04T20:59:08.8112893Z     Args:
2025-03-04T20:59:08.8113145Z         mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2025-03-04T20:59:08.8113358Z         mesh dimension of the DeviceMesh to create the submesh for.
2025-03-04T20:59:08.8113455Z     Returns:
2025-03-04T20:59:08.8113573Z         A :class:`DeviceMesh` object
2025-03-04T20:59:08.8113785Z     
2025-03-04T20:59:08.8114081Z     The following program runs on each process/rank in an SPMD manner in a world size of 8.
2025-03-04T20:59:08.8114207Z     In the first example:
2025-03-04T20:59:08.8114496Z         Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2025-03-04T20:59:08.8114769Z         Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2025-03-04T20:59:08.8115005Z         Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2025-03-04T20:59:08.8115254Z         Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2025-03-04T20:59:08.8115485Z         Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2025-03-04T20:59:08.8115730Z         Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2025-03-04T20:59:08.8115821Z     
2025-03-04T20:59:08.8115941Z     In the second example:
2025-03-04T20:59:08.8116221Z         Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2025-03-04T20:59:08.8116505Z         Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2025-03-04T20:59:08.8116778Z         Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2025-03-04T20:59:08.8117059Z         Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2025-03-04T20:59:08.8117146Z     
2025-03-04T20:59:08.8117268Z     Example::
2025-03-04T20:59:08.8117413Z         >>> # xdoctest: +SKIP("no rank")
2025-03-04T20:59:08.8117605Z         >>> from torch.distributed.device_mesh import DeviceMesh
2025-03-04T20:59:08.8117696Z         >>>
2025-03-04T20:59:08.8117918Z         >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2025-03-04T20:59:08.8118070Z         >>> # of cross-host(dim 0), and within-host (dim 1).
2025-03-04T20:59:08.8118347Z         >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-04T20:59:08.8118457Z         >>> tp_mesh = mesh_2d["tp"]
2025-03-04T20:59:08.8118577Z         >>> dp_mesh = mesh_2d["dp"]
2025-03-04T20:59:08.8118670Z         >>>
2025-03-04T20:59:08.8118795Z         >>> # Initialize a 3D mesh.
2025-03-04T20:59:08.8119080Z         >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2025-03-04T20:59:08.8119403Z         >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2025-03-04T20:59:08.8119530Z         >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2025-03-04T20:59:08.8119664Z         >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2025-03-04T20:59:08.8119753Z     
2025-03-04T20:59:08.8120447Z     Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2025-03-04T20:59:08.8120566Z     
2025-03-04T20:59:08.8120837Z     mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-03-04T20:59:08.8120966Z                                                                                      ^
2025-03-04T20:59:08.8121085Z       warnings.warn(msg)
2025-03-04T20:59:08.8121175Z     
2025-03-04T20:59:08.8121400Z --- Parse Warning: 37 / 116 ---
2025-03-04T20:59:08.8122377Z     /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=2610.
2025-03-04T20:59:08.8122665Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8122753Z     
2025-03-04T20:59:08.8123030Z     Send or Receive a batch of tensors asynchronously and return a list of requests.
2025-03-04T20:59:08.8123120Z     
2025-03-04T20:59:08.8123418Z     Process each of the operations in ``p2p_op_list`` and return the corresponding
2025-03-04T20:59:08.8123625Z     requests. NCCL, Gloo, and UCC backend are currently supported.
2025-03-04T20:59:08.8123726Z     
2025-03-04T20:59:08.8123854Z     Args:
2025-03-04T20:59:08.8124103Z         p2p_op_list: A list of point-to-point operations(type of each operator is
2025-03-04T20:59:08.8124340Z             ``torch.distributed.P2POp``). The order of the isend/irecv in the list
2025-03-04T20:59:08.8124570Z             matters and it needs to match with corresponding isend/irecv on the
2025-03-04T20:59:08.8124669Z             remote end.
2025-03-04T20:59:08.8124770Z     
2025-03-04T20:59:08.8124863Z     Returns:
2025-03-04T20:59:08.8125129Z         A list of distributed request objects returned by calling the corresponding
2025-03-04T20:59:08.8125236Z         op in the op_list.
2025-03-04T20:59:08.8125338Z     
2025-03-04T20:59:08.8125433Z     Examples:
2025-03-04T20:59:08.8125564Z         >>> # xdoctest: +SKIP("no rank")
2025-03-04T20:59:08.8125763Z         >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
2025-03-04T20:59:08.8125936Z         >>> recv_tensor = torch.randn(2, dtype=torch.float32)
2025-03-04T20:59:08.8126160Z         >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
2025-03-04T20:59:08.8126283Z         >>> recv_op = dist.P2POp(
2025-03-04T20:59:08.8126486Z         ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
2025-03-04T20:59:08.8126590Z         ... )
2025-03-04T20:59:08.8126735Z         >>> reqs = batch_isend_irecv([send_op, recv_op])
2025-03-04T20:59:08.8126882Z         >>> for req in reqs:
2025-03-04T20:59:08.8126985Z         >>>     req.wait()
2025-03-04T20:59:08.8127095Z         >>> recv_tensor
2025-03-04T20:59:08.8127203Z         tensor([2, 3])     # Rank 0
2025-03-04T20:59:08.8127321Z         tensor([0, 1])     # Rank 1
2025-03-04T20:59:08.8127412Z     
2025-03-04T20:59:08.8127677Z     .. note:: Note that when this API is used with the NCCL PG backend, users must set
2025-03-04T20:59:08.8127911Z         the current GPU device with `torch.cuda.set_device`, otherwise it will
2025-03-04T20:59:08.8128046Z         lead to unexpected hang issues.
2025-03-04T20:59:08.8128135Z     
2025-03-04T20:59:08.8128354Z         In addition, if this API is the first collective call in the ``group``
2025-03-04T20:59:08.8128598Z         passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
2025-03-04T20:59:08.8128842Z         this API call; otherwise, the behavior is undefined. If this API call is
2025-03-04T20:59:08.8129073Z         not the first collective call in the ``group``, batched P2P operations
2025-03-04T20:59:08.8129291Z         involving only a subset of ranks of the ``group`` are allowed.
2025-03-04T20:59:08.8129379Z     
2025-03-04T20:59:08.8129638Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8129765Z     
2025-03-04T20:59:08.8129870Z       warnings.warn(msg)
2025-03-04T20:59:08.8129972Z     
2025-03-04T20:59:08.8130168Z --- Parse Warning: 38 / 116 ---
2025-03-04T20:59:08.8131128Z     /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=2735.
2025-03-04T20:59:08.8131400Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8131499Z     
2025-03-04T20:59:08.8131775Z     Reduces the tensor data across all machines in a way that all get the final result.
2025-03-04T20:59:08.8131878Z     
2025-03-04T20:59:08.8132112Z     After the call ``tensor`` is going to be bitwise identical in all processes.
2025-03-04T20:59:08.8132212Z     
2025-03-04T20:59:08.8132332Z     Complex tensors are supported.
2025-03-04T20:59:08.8132436Z     
2025-03-04T20:59:08.8132527Z     Args:
2025-03-04T20:59:08.8132753Z         tensor (Tensor): Input and output of the collective. The function
2025-03-04T20:59:08.8132893Z             operates in-place.
2025-03-04T20:59:08.8133031Z         op (optional): One of the values from
2025-03-04T20:59:08.8133159Z             ``torch.distributed.ReduceOp``
2025-03-04T20:59:08.8133407Z             enum.  Specifies an operation used for element-wise reductions.
2025-03-04T20:59:08.8133650Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:59:08.8133800Z             the default process group will be used.
2025-03-04T20:59:08.8134003Z         async_op (bool, optional): Whether this op should be an async op
2025-03-04T20:59:08.8134104Z     
2025-03-04T20:59:08.8134241Z     Returns:
2025-03-04T20:59:08.8134402Z         Async work handle, if async_op is set to True.
2025-03-04T20:59:08.8134556Z         None, if not async_op or if not part of the group
2025-03-04T20:59:08.8134658Z     
2025-03-04T20:59:08.8134752Z     Examples:
2025-03-04T20:59:08.8134879Z         >>> # xdoctest: +SKIP("no rank")
2025-03-04T20:59:08.8135026Z         >>> # All tensors below are of torch.int64 type.
2025-03-04T20:59:08.8135164Z         >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:59:08.8135297Z         >>> device = torch.device(f"cuda:{rank}")
2025-03-04T20:59:08.8135546Z         >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-04T20:59:08.8135640Z         >>> tensor
2025-03-04T20:59:08.8135780Z         tensor([1, 2], device='cuda:0') # Rank 0
2025-03-04T20:59:08.8135902Z         tensor([3, 4], device='cuda:1') # Rank 1
2025-03-04T20:59:08.8136058Z         >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-04T20:59:08.8136177Z         >>> tensor
2025-03-04T20:59:08.8136314Z         tensor([4, 6], device='cuda:0') # Rank 0
2025-03-04T20:59:08.8136436Z         tensor([4, 6], device='cuda:1') # Rank 1
2025-03-04T20:59:08.8136540Z     
2025-03-04T20:59:08.8136689Z         >>> # All tensors below are of torch.cfloat type.
2025-03-04T20:59:08.8136827Z         >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:59:08.8136944Z         >>> tensor = torch.tensor(
2025-03-04T20:59:08.8137101Z         ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-04T20:59:08.8137221Z         ... ) + 2 * rank * (1 + 1j)
2025-03-04T20:59:08.8137317Z         >>> tensor
2025-03-04T20:59:08.8137483Z         tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-04T20:59:08.8137629Z         tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-04T20:59:08.8137874Z         >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-03-04T20:59:08.8137970Z         >>> tensor
2025-03-04T20:59:08.8138136Z         tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
2025-03-04T20:59:08.8138281Z         tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
2025-03-04T20:59:08.8138381Z     
2025-03-04T20:59:08.8138471Z     
2025-03-04T20:59:08.8138780Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8138868Z     
2025-03-04T20:59:08.8138987Z       warnings.warn(msg)
2025-03-04T20:59:08.8139076Z     
2025-03-04T20:59:08.8139294Z --- Parse Warning: 39 / 116 ---
2025-03-04T20:59:08.8140250Z     /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=3079.
2025-03-04T20:59:08.8140537Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8140627Z     
2025-03-04T20:59:08.8140874Z     Gathers picklable objects from the whole group in a single process.
2025-03-04T20:59:08.8140963Z     
2025-03-04T20:59:08.8141225Z     Similar to :func:`gather`, but Python objects can be passed in. Note that the
2025-03-04T20:59:08.8141382Z     object must be picklable in order to be gathered.
2025-03-04T20:59:08.8141483Z     
2025-03-04T20:59:08.8141575Z     Args:
2025-03-04T20:59:08.8141726Z         obj (Any): Input object. Must be picklable.
2025-03-04T20:59:08.8141975Z         object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2025-03-04T20:59:08.8142181Z             should be correctly sized as the size of the group for this
2025-03-04T20:59:08.8142431Z             collective and will contain the output. Must be ``None`` on non-dst
2025-03-04T20:59:08.8142563Z             ranks. (default is ``None``)
2025-03-04T20:59:08.8142892Z         dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
2025-03-04T20:59:08.8143108Z             (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
2025-03-04T20:59:08.8143350Z         group: (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:59:08.8143561Z             the default process group will be used. Default is ``None``.
2025-03-04T20:59:08.8143918Z         group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
2025-03-04T20:59:08.8144024Z     
2025-03-04T20:59:08.8144116Z     Returns:
2025-03-04T20:59:08.8144319Z         None. On the ``dst`` rank, ``object_gather_list`` will contain the
2025-03-04T20:59:08.8144434Z         output of the collective.
2025-03-04T20:59:08.8144542Z     
2025-03-04T20:59:08.8144767Z     .. note:: Note that this API differs slightly from the gather collective
2025-03-04T20:59:08.8145027Z         since it does not provide an async_op handle and thus will be a blocking
2025-03-04T20:59:08.8145118Z         call.
2025-03-04T20:59:08.8145219Z     
2025-03-04T20:59:08.8145489Z     .. note:: For NCCL-based processed groups, internal tensor representations
2025-03-04T20:59:08.8145724Z         of objects must be moved to the GPU device before communication takes
2025-03-04T20:59:08.8145878Z         place. In this case, the device used is given by
2025-03-04T20:59:08.8146123Z         ``torch.cuda.current_device()`` and it is the user's responsiblity to
2025-03-04T20:59:08.8146338Z         ensure that this is set so that each rank has an individual GPU, via
2025-03-04T20:59:08.8146466Z         ``torch.cuda.set_device()``.
2025-03-04T20:59:08.8146558Z     
2025-03-04T20:59:08.8146671Z     .. warning::
2025-03-04T20:59:08.8146877Z         :func:`gather_object` uses ``pickle`` module implicitly, which is
2025-03-04T20:59:08.8147120Z         known to be insecure. It is possible to construct malicious pickle data
2025-03-04T20:59:08.8147350Z         which will execute arbitrary code during unpickling. Only call this
2025-03-04T20:59:08.8147478Z         function with data you trust.
2025-03-04T20:59:08.8147564Z     
2025-03-04T20:59:08.8147674Z     .. warning::
2025-03-04T20:59:08.8147899Z         Calling :func:`gather_object` with GPU tensors is not well supported
2025-03-04T20:59:08.8148142Z         and inefficient as it incurs GPU -> CPU transfer since tensors would be
2025-03-04T20:59:08.8148350Z         pickled. Please consider using :func:`gather` instead.
2025-03-04T20:59:08.8148442Z     
2025-03-04T20:59:08.8148540Z     Example::
2025-03-04T20:59:08.8148682Z         >>> # xdoctest: +SKIP("need process group init")
2025-03-04T20:59:08.8148883Z         >>> # Note: Process group initialization omitted on each rank.
2025-03-04T20:59:08.8149009Z         >>> import torch.distributed as dist
2025-03-04T20:59:08.8149136Z         >>> # Assumes world_size of 3.
2025-03-04T20:59:08.8149318Z         >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2025-03-04T20:59:08.8149460Z         >>> output = [None for _ in gather_objects]
2025-03-04T20:59:08.8149566Z         >>> dist.gather_object(
2025-03-04T20:59:08.8149705Z         ...     gather_objects[dist.get_rank()],
2025-03-04T20:59:08.8149845Z         ...     output if dist.get_rank() == 0 else None,
2025-03-04T20:59:08.8149950Z         ...     dst=0
2025-03-04T20:59:08.8150038Z         ... )
2025-03-04T20:59:08.8150146Z         >>> # On rank 0
2025-03-04T20:59:08.8150238Z         >>> output
2025-03-04T20:59:08.8150346Z         ['foo', 12, {1: 2}]
2025-03-04T20:59:08.8150461Z     
2025-03-04T20:59:08.8150735Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8150823Z     
2025-03-04T20:59:08.8150936Z       warnings.warn(msg)
2025-03-04T20:59:08.8151021Z     
2025-03-04T20:59:08.8151257Z --- Parse Warning: 40 / 116 ---
2025-03-04T20:59:08.8152201Z     /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=3655.
2025-03-04T20:59:08.8152487Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8152573Z     
2025-03-04T20:59:08.8152737Z     Gathers tensors from the whole group in a list.
2025-03-04T20:59:08.8152824Z     
2025-03-04T20:59:08.8152990Z     Complex and uneven sized tensors are supported.
2025-03-04T20:59:08.8153078Z     
2025-03-04T20:59:08.8153178Z     Args:
2025-03-04T20:59:08.8153369Z         tensor_list (list[Tensor]): Output list. It should contain
2025-03-04T20:59:08.8153606Z             correctly-sized tensors to be used for output of the collective.
2025-03-04T20:59:08.8153740Z             Uneven sized tensors are supported.
2025-03-04T20:59:08.8153947Z         tensor (Tensor): Tensor to be broadcast from current process.
2025-03-04T20:59:08.8154187Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:59:08.8154329Z             the default process group will be used.
2025-03-04T20:59:08.8154532Z         async_op (bool, optional): Whether this op should be an async op
2025-03-04T20:59:08.8154653Z     
2025-03-04T20:59:08.8154748Z     Returns:
2025-03-04T20:59:08.8154909Z         Async work handle, if async_op is set to True.
2025-03-04T20:59:08.8155066Z         None, if not async_op or if not part of the group
2025-03-04T20:59:08.8155165Z     
2025-03-04T20:59:08.8155258Z     Examples:
2025-03-04T20:59:08.8155402Z         >>> # xdoctest: +SKIP("need process group init")
2025-03-04T20:59:08.8155559Z         >>> # All tensors below are of torch.int64 dtype.
2025-03-04T20:59:08.8155684Z         >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:59:08.8155823Z         >>> device = torch.device(f"cuda:{rank}")
2025-03-04T20:59:08.8155923Z         >>> tensor_list = [
2025-03-04T20:59:08.8156152Z         ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
2025-03-04T20:59:08.8156240Z         ... ]
2025-03-04T20:59:08.8156350Z         >>> tensor_list
2025-03-04T20:59:08.8156555Z         [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
2025-03-04T20:59:08.8156770Z         [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
2025-03-04T20:59:08.8157003Z         >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-03-04T20:59:08.8157150Z         >>> tensor
2025-03-04T20:59:08.8157271Z         tensor([1, 2], device='cuda:0') # Rank 0
2025-03-04T20:59:08.8157408Z         tensor([3, 4], device='cuda:1') # Rank 1
2025-03-04T20:59:08.8157532Z         >>> dist.all_gather(tensor_list, tensor)
2025-03-04T20:59:08.8157640Z         >>> tensor_list
2025-03-04T20:59:08.8157846Z         [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
2025-03-04T20:59:08.8158061Z         [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
2025-03-04T20:59:08.8158146Z     
2025-03-04T20:59:08.8158309Z         >>> # All tensors below are of torch.cfloat dtype.
2025-03-04T20:59:08.8158435Z         >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:59:08.8158551Z         >>> tensor_list = [
2025-03-04T20:59:08.8158778Z         ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
2025-03-04T20:59:08.8158882Z         ... ]
2025-03-04T20:59:08.8158979Z         >>> tensor_list
2025-03-04T20:59:08.8159249Z         [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
2025-03-04T20:59:08.8159538Z         [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
2025-03-04T20:59:08.8159660Z         >>> tensor = torch.tensor(
2025-03-04T20:59:08.8159840Z         ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-03-04T20:59:08.8159955Z         ... ) + 2 * rank * (1 + 1j)
2025-03-04T20:59:08.8160048Z         >>> tensor
2025-03-04T20:59:08.8160208Z         tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-03-04T20:59:08.8160353Z         tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-03-04T20:59:08.8160495Z         >>> dist.all_gather(tensor_list, tensor)
2025-03-04T20:59:08.8160595Z         >>> tensor_list
2025-03-04T20:59:08.8160851Z         [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
2025-03-04T20:59:08.8161101Z         [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
2025-03-04T20:59:08.8161203Z     
2025-03-04T20:59:08.8161297Z     
2025-03-04T20:59:08.8161571Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8161660Z     
2025-03-04T20:59:08.8161779Z       warnings.warn(msg)
2025-03-04T20:59:08.8161869Z     
2025-03-04T20:59:08.8162085Z --- Parse Warning: 41 / 116 ---
2025-03-04T20:59:08.8163051Z     /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=4343.
2025-03-04T20:59:08.8163360Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8163451Z     
2025-03-04T20:59:08.8163723Z     Split input tensor and then scatter the split list to all processes in a group.
2025-03-04T20:59:08.8163812Z     
2025-03-04T20:59:08.8164091Z     Later the received tensors are concatenated from all the processes in the group
2025-03-04T20:59:08.8164221Z     and returned as a single output tensor.
2025-03-04T20:59:08.8164320Z     
2025-03-04T20:59:08.8164438Z     Complex tensors are supported.
2025-03-04T20:59:08.8164532Z     
2025-03-04T20:59:08.8164623Z     Args:
2025-03-04T20:59:08.8164812Z         output (Tensor): Gathered concatenated output tensor.
2025-03-04T20:59:08.8164946Z         input (Tensor): Input tensor to scatter.
2025-03-04T20:59:08.8165178Z         output_split_sizes: (list[Int], optional): Output split sizes for dim 0
2025-03-04T20:59:08.8165405Z             if specified None or empty, dim 0 of ``output`` tensor must divide
2025-03-04T20:59:08.8165522Z             equally by ``world_size``.
2025-03-04T20:59:08.8165754Z         input_split_sizes: (list[Int], optional): Input split sizes for dim 0
2025-03-04T20:59:08.8165970Z             if specified None or empty, dim 0 of ``input`` tensor must divide
2025-03-04T20:59:08.8166113Z             equally by ``world_size``.
2025-03-04T20:59:08.8166352Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:59:08.8166499Z             the default process group will be used.
2025-03-04T20:59:08.8166705Z         async_op (bool, optional): Whether this op should be an async op.
2025-03-04T20:59:08.8166807Z     
2025-03-04T20:59:08.8166903Z     Returns:
2025-03-04T20:59:08.8167064Z         Async work handle, if async_op is set to True.
2025-03-04T20:59:08.8167219Z         None, if not async_op or if not part of the group.
2025-03-04T20:59:08.8167316Z     
2025-03-04T20:59:08.8167416Z     .. warning::
2025-03-04T20:59:08.8167607Z         `all_to_all_single` is experimental and subject to change.
2025-03-04T20:59:08.8167696Z     
2025-03-04T20:59:08.8167799Z     Examples:
2025-03-04T20:59:08.8167931Z         >>> # xdoctest: +SKIP("Undefined rank")
2025-03-04T20:59:08.8168060Z         >>> input = torch.arange(4) + rank * 4
2025-03-04T20:59:08.8168153Z         >>> input
2025-03-04T20:59:08.8168272Z         tensor([0, 1, 2, 3])     # Rank 0
2025-03-04T20:59:08.8168406Z         tensor([4, 5, 6, 7])     # Rank 1
2025-03-04T20:59:08.8168519Z         tensor([8, 9, 10, 11])   # Rank 2
2025-03-04T20:59:08.8168625Z         tensor([12, 13, 14, 15]) # Rank 3
2025-03-04T20:59:08.8168806Z         >>> output = torch.empty([4], dtype=torch.int64)
2025-03-04T20:59:08.8168939Z         >>> dist.all_to_all_single(output, input)
2025-03-04T20:59:08.8169042Z         >>> output
2025-03-04T20:59:08.8169149Z         tensor([0, 4, 8, 12])    # Rank 0
2025-03-04T20:59:08.8169267Z         tensor([1, 5, 9, 13])    # Rank 1
2025-03-04T20:59:08.8169374Z         tensor([2, 6, 10, 14])   # Rank 2
2025-03-04T20:59:08.8169495Z         tensor([3, 7, 11, 15])   # Rank 3
2025-03-04T20:59:08.8169583Z     
2025-03-04T20:59:08.8169765Z         >>> # Essentially, it is similar to following operation:
2025-03-04T20:59:08.8169917Z         >>> scatter_list = list(input.chunk(world_size))
2025-03-04T20:59:08.8170079Z         >>> gather_list = list(output.chunk(world_size))
2025-03-04T20:59:08.8170200Z         >>> for i in range(world_size):
2025-03-04T20:59:08.8170451Z         >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
2025-03-04T20:59:08.8170539Z     
2025-03-04T20:59:08.8170684Z         >>> # Another example with uneven split
2025-03-04T20:59:08.8170779Z         >>> input
2025-03-04T20:59:08.8170953Z         tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-04T20:59:08.8171121Z         tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-04T20:59:08.8171296Z         tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-04T20:59:08.8171493Z         tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-04T20:59:08.8171609Z         >>> input_splits
2025-03-04T20:59:08.8171746Z         [2, 2, 1, 1]                                                     # Rank 0
2025-03-04T20:59:08.8171889Z         [3, 2, 2, 2]                                                     # Rank 1
2025-03-04T20:59:08.8172020Z         [2, 1, 1, 1]                                                     # Rank 2
2025-03-04T20:59:08.8172161Z         [2, 2, 2, 1]                                                     # Rank 3
2025-03-04T20:59:08.8172314Z         >>> output_splits
2025-03-04T20:59:08.8172457Z         [2, 3, 2, 2]                                                     # Rank 0
2025-03-04T20:59:08.8172643Z         [2, 2, 1, 2]                                                     # Rank 1
2025-03-04T20:59:08.8172785Z         [1, 2, 1, 2]                                                     # Rank 2
2025-03-04T20:59:08.8172915Z         [1, 2, 1, 1]                                                     # Rank 3
2025-03-04T20:59:08.8173026Z         >>> output = ...
2025-03-04T20:59:08.8173243Z         >>> dist.all_to_all_single(output, input, output_splits, input_splits)
2025-03-04T20:59:08.8173377Z         >>> output
2025-03-04T20:59:08.8173545Z         tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
2025-03-04T20:59:08.8173959Z         tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
2025-03-04T20:59:08.8174127Z         tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
2025-03-04T20:59:08.8174301Z         tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
2025-03-04T20:59:08.8174390Z     
2025-03-04T20:59:08.8174477Z     
2025-03-04T20:59:08.8174658Z         >>> # Another example with tensors of torch.cfloat type.
2025-03-04T20:59:08.8174768Z         >>> input = torch.tensor(
2025-03-04T20:59:08.8174928Z         ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-04T20:59:08.8175033Z         ... ) + 4 * rank * (1 + 1j)
2025-03-04T20:59:08.8175134Z         >>> input
2025-03-04T20:59:08.8175315Z         tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
2025-03-04T20:59:08.8175502Z         tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
2025-03-04T20:59:08.8175786Z         tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
2025-03-04T20:59:08.8175989Z         tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
2025-03-04T20:59:08.8176183Z         >>> output = torch.empty([4], dtype=torch.int64)
2025-03-04T20:59:08.8176327Z         >>> dist.all_to_all_single(output, input)
2025-03-04T20:59:08.8176419Z         >>> output
2025-03-04T20:59:08.8176612Z         tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
2025-03-04T20:59:08.8176795Z         tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
2025-03-04T20:59:08.8176986Z         tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
2025-03-04T20:59:08.8177167Z         tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
2025-03-04T20:59:08.8177269Z     
2025-03-04T20:59:08.8177533Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8177629Z     
2025-03-04T20:59:08.8177816Z       warnings.warn(msg)
2025-03-04T20:59:08.8177914Z     
2025-03-04T20:59:08.8178145Z --- Parse Warning: 42 / 116 ---
2025-03-04T20:59:08.8179095Z     /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=4481.
2025-03-04T20:59:08.8179366Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8179465Z     
2025-03-04T20:59:08.8179883Z     Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
2025-03-04T20:59:08.8179985Z     
2025-03-04T20:59:08.8180105Z     Complex tensors are supported.
2025-03-04T20:59:08.8180205Z     
2025-03-04T20:59:08.8180339Z     Args:
2025-03-04T20:59:08.8180580Z         output_tensor_list (list[Tensor]): List of tensors to be gathered one
2025-03-04T20:59:08.8180678Z             per rank.
2025-03-04T20:59:08.8180930Z         input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
2025-03-04T20:59:08.8181170Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:59:08.8181320Z             the default process group will be used.
2025-03-04T20:59:08.8181523Z         async_op (bool, optional): Whether this op should be an async op.
2025-03-04T20:59:08.8181621Z     
2025-03-04T20:59:08.8181718Z     Returns:
2025-03-04T20:59:08.8181879Z         Async work handle, if async_op is set to True.
2025-03-04T20:59:08.8182035Z         None, if not async_op or if not part of the group.
2025-03-04T20:59:08.8182134Z     
2025-03-04T20:59:08.8182234Z     .. warning::
2025-03-04T20:59:08.8182400Z         `all_to_all` is experimental and subject to change.
2025-03-04T20:59:08.8182521Z     
2025-03-04T20:59:08.8182627Z     Examples:
2025-03-04T20:59:08.8182754Z         >>> # xdoctest: +SKIP("Undefined rank")
2025-03-04T20:59:08.8182890Z         >>> input = torch.arange(4) + rank * 4
2025-03-04T20:59:08.8183007Z         >>> input = list(input.chunk(4))
2025-03-04T20:59:08.8183110Z         >>> input
2025-03-04T20:59:08.8183286Z         [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
2025-03-04T20:59:08.8183468Z         [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
2025-03-04T20:59:08.8183639Z         [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
2025-03-04T20:59:08.8183824Z         [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
2025-03-04T20:59:08.8184014Z         >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-04T20:59:08.8184149Z         >>> dist.all_to_all(output, input)
2025-03-04T20:59:08.8184241Z         >>> output
2025-03-04T20:59:08.8184427Z         [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
2025-03-04T20:59:08.8184599Z         [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
2025-03-04T20:59:08.8184812Z         [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
2025-03-04T20:59:08.8184981Z         [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
2025-03-04T20:59:08.8185081Z     
2025-03-04T20:59:08.8185277Z         >>> # Essentially, it is similar to following operation:
2025-03-04T20:59:08.8185387Z         >>> scatter_list = input
2025-03-04T20:59:08.8185504Z         >>> gather_list = output
2025-03-04T20:59:08.8185619Z         >>> for i in range(world_size):
2025-03-04T20:59:08.8185859Z         >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
2025-03-04T20:59:08.8185951Z     
2025-03-04T20:59:08.8186054Z         >>> input
2025-03-04T20:59:08.8186215Z         tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-03-04T20:59:08.8186391Z         tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-03-04T20:59:08.8186552Z         tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-03-04T20:59:08.8186728Z         tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-03-04T20:59:08.8186827Z         >>> input_splits
2025-03-04T20:59:08.8186968Z         [2, 2, 1, 1]                                                     # Rank 0
2025-03-04T20:59:08.8187094Z         [3, 2, 2, 2]                                                     # Rank 1
2025-03-04T20:59:08.8187228Z         [2, 1, 1, 1]                                                     # Rank 2
2025-03-04T20:59:08.8187352Z         [2, 2, 2, 1]                                                     # Rank 3
2025-03-04T20:59:08.8187489Z         >>> output_splits
2025-03-04T20:59:08.8187615Z         [2, 3, 2, 2]                                                     # Rank 0
2025-03-04T20:59:08.8187753Z         [2, 2, 1, 2]                                                     # Rank 1
2025-03-04T20:59:08.8187877Z         [1, 2, 1, 2]                                                     # Rank 2
2025-03-04T20:59:08.8188015Z         [1, 2, 1, 1]                                                     # Rank 3
2025-03-04T20:59:08.8188151Z         >>> input = list(input.split(input_splits))
2025-03-04T20:59:08.8188258Z         >>> input
2025-03-04T20:59:08.8188469Z         [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
2025-03-04T20:59:08.8188687Z         [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
2025-03-04T20:59:08.8188895Z         [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
2025-03-04T20:59:08.8189115Z         [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
2025-03-04T20:59:08.8189214Z         >>> output = ...
2025-03-04T20:59:08.8189345Z         >>> dist.all_to_all(output, input)
2025-03-04T20:59:08.8189485Z         >>> output
2025-03-04T20:59:08.8189704Z         [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
2025-03-04T20:59:08.8189912Z         [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
2025-03-04T20:59:08.8190137Z         [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
2025-03-04T20:59:08.8190348Z         [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
2025-03-04T20:59:08.8190448Z     
2025-03-04T20:59:08.8190616Z         >>> # Another example with tensors of torch.cfloat type.
2025-03-04T20:59:08.8190738Z         >>> input = torch.tensor(
2025-03-04T20:59:08.8190884Z         ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-03-04T20:59:08.8191000Z         ... ) + 4 * rank * (1 + 1j)
2025-03-04T20:59:08.8191114Z         >>> input = list(input.chunk(4))
2025-03-04T20:59:08.8191219Z         >>> input
2025-03-04T20:59:08.8191441Z         [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
2025-03-04T20:59:08.8191672Z         [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
2025-03-04T20:59:08.8191920Z         [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
2025-03-04T20:59:08.8192182Z         [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
2025-03-04T20:59:08.8192373Z         >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-03-04T20:59:08.8192508Z         >>> dist.all_to_all(output, input)
2025-03-04T20:59:08.8192600Z         >>> output
2025-03-04T20:59:08.8192831Z         [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
2025-03-04T20:59:08.8193046Z         [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
2025-03-04T20:59:08.8193279Z         [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
2025-03-04T20:59:08.8193497Z         [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
2025-03-04T20:59:08.8193600Z     
2025-03-04T20:59:08.8193686Z     
2025-03-04T20:59:08.8193960Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8194044Z     
2025-03-04T20:59:08.8194165Z       warnings.warn(msg)
2025-03-04T20:59:08.8194253Z     
2025-03-04T20:59:08.8194470Z --- Parse Warning: 43 / 116 ---
2025-03-04T20:59:08.8195325Z     /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.
2025-03-04T20:59:08.8195638Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8195727Z     
2025-03-04T20:59:08.8195867Z     Module ``torch.distributed.launch``.
2025-03-04T20:59:08.8195957Z     
2025-03-04T20:59:08.8196228Z     ``torch.distributed.launch`` is a module that spawns up multiple distributed
2025-03-04T20:59:08.8196389Z     training processes on each of the training nodes.
2025-03-04T20:59:08.8196486Z     
2025-03-04T20:59:08.8196582Z     .. warning::
2025-03-04T20:59:08.8196679Z     
2025-03-04T20:59:08.8196945Z         This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2025-03-04T20:59:08.8197042Z     
2025-03-04T20:59:08.8197292Z     The utility can be used for single-node distributed training, in which one or
2025-03-04T20:59:08.8197551Z     more processes per node will be spawned. The utility can be used for either
2025-03-04T20:59:08.8197778Z     CPU training or GPU training. If the utility is used for GPU training,
2025-03-04T20:59:08.8198042Z     each distributed process will be operating on a single GPU. This can achieve
2025-03-04T20:59:08.8198285Z     well-improved single-node training performance. It can also be used in
2025-03-04T20:59:08.8198598Z     multi-node distributed training, by spawning up multiple processes on each node
2025-03-04T20:59:08.8198844Z     for well-improved multi-node distributed training performance as well.
2025-03-04T20:59:08.8199100Z     This will especially be beneficial for systems with multiple Infiniband
2025-03-04T20:59:08.8199366Z     interfaces that have direct-GPU support, since all of them can be utilized for
2025-03-04T20:59:08.8199507Z     aggregated communication bandwidth.
2025-03-04T20:59:08.8199596Z     
2025-03-04T20:59:08.8199857Z     In both cases of single-node distributed training or multi-node distributed
2025-03-04T20:59:08.8200104Z     training, this utility will launch the given number of processes per node
2025-03-04T20:59:08.8200357Z     (``--nproc-per-node``). If used for GPU training, this number needs to be less
2025-03-04T20:59:08.8200587Z     or equal to the number of GPUs on the current system (``nproc_per_node``),
2025-03-04T20:59:08.8200811Z     and each process will be operating on a single GPU from *GPU 0 to
2025-03-04T20:59:08.8200934Z     GPU (nproc_per_node - 1)*.
2025-03-04T20:59:08.8201039Z     
2025-03-04T20:59:08.8201179Z     **How to use this module:**
2025-03-04T20:59:08.8201283Z     
2025-03-04T20:59:08.8201446Z     1. Single-Node multi-process distributed training
2025-03-04T20:59:08.8201550Z     
2025-03-04T20:59:08.8201650Z     ::
2025-03-04T20:59:08.8201768Z     
2025-03-04T20:59:08.8202033Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-04T20:59:08.8202233Z                    YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2025-03-04T20:59:08.8202383Z                    arguments of your training script)
2025-03-04T20:59:08.8202473Z     
2025-03-04T20:59:08.8202707Z     2. Multi-Node multi-process distributed training: (e.g. two nodes)
2025-03-04T20:59:08.8202798Z     
2025-03-04T20:59:08.8202905Z     
2025-03-04T20:59:08.8203057Z     Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2025-03-04T20:59:08.8203161Z     
2025-03-04T20:59:08.8203257Z     ::
2025-03-04T20:59:08.8203357Z     
2025-03-04T20:59:08.8203598Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-04T20:59:08.8203783Z                    --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2025-03-04T20:59:08.8204003Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-04T20:59:08.8204180Z                    and all other arguments of your training script)
2025-03-04T20:59:08.8204268Z     
2025-03-04T20:59:08.8204374Z     Node 2:
2025-03-04T20:59:08.8204461Z     
2025-03-04T20:59:08.8204560Z     ::
2025-03-04T20:59:08.8204652Z     
2025-03-04T20:59:08.8204902Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-03-04T20:59:08.8205093Z                    --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2025-03-04T20:59:08.8205321Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-03-04T20:59:08.8205481Z                    and all other arguments of your training script)
2025-03-04T20:59:08.8205582Z     
2025-03-04T20:59:08.8205752Z     3. To look up what optional arguments this module offers:
2025-03-04T20:59:08.8205851Z     
2025-03-04T20:59:08.8205942Z     ::
2025-03-04T20:59:08.8206042Z     
2025-03-04T20:59:08.8206187Z         python -m torch.distributed.launch --help
2025-03-04T20:59:08.8206285Z     
2025-03-04T20:59:08.8206373Z     
2025-03-04T20:59:08.8206480Z     **Important Notices:**
2025-03-04T20:59:08.8206577Z     
2025-03-04T20:59:08.8206773Z     1. This utility and multi-process distributed (single-node or
2025-03-04T20:59:08.8207043Z     multi-node) GPU training currently only achieves the best performance using
2025-03-04T20:59:08.8207306Z     the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2025-03-04T20:59:08.8207423Z     use for GPU training.
2025-03-04T20:59:08.8207511Z     
2025-03-04T20:59:08.8207745Z     2. In your training program, you must parse the command-line argument:
2025-03-04T20:59:08.8208014Z     ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2025-03-04T20:59:08.8208269Z     If your training program uses GPUs, you should ensure that your code only
2025-03-04T20:59:08.8208470Z     runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2025-03-04T20:59:08.8208570Z     
2025-03-04T20:59:08.8208689Z     Parsing the local_rank argument
2025-03-04T20:59:08.8208789Z     
2025-03-04T20:59:08.8208880Z     ::
2025-03-04T20:59:08.8208980Z     
2025-03-04T20:59:08.8209088Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8209206Z         >>> import argparse
2025-03-04T20:59:08.8209342Z         >>> parser = argparse.ArgumentParser()
2025-03-04T20:59:08.8209558Z         >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2025-03-04T20:59:08.8209676Z         >>> args = parser.parse_args()
2025-03-04T20:59:08.8209777Z     
2025-03-04T20:59:08.8209912Z     Set your device to local rank using either
2025-03-04T20:59:08.8210015Z     
2025-03-04T20:59:08.8210105Z     ::
2025-03-04T20:59:08.8210205Z     
2025-03-04T20:59:08.8210414Z         >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2025-03-04T20:59:08.8210538Z     
2025-03-04T20:59:08.8210628Z     or
2025-03-04T20:59:08.8210727Z     
2025-03-04T20:59:08.8210817Z     ::
2025-03-04T20:59:08.8210906Z     
2025-03-04T20:59:08.8211084Z         >>> with torch.cuda.device(args.local_rank):
2025-03-04T20:59:08.8211193Z         >>>    # your code to run
2025-03-04T20:59:08.8211297Z         >>>    ...
2025-03-04T20:59:08.8211385Z     
2025-03-04T20:59:08.8211507Z     .. versionchanged:: 2.0.0
2025-03-04T20:59:08.8211597Z     
2025-03-04T20:59:08.8211869Z         The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2025-03-04T20:59:08.8212119Z         From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2025-03-04T20:59:08.8212284Z         previously used underscored ``--local_rank``.
2025-03-04T20:59:08.8212375Z     
2025-03-04T20:59:08.8212634Z         For backward compatibility, it may be necessary for users to handle both
2025-03-04T20:59:08.8212913Z         cases in their argument parsing code. This means including both ``"--local-rank"``
2025-03-04T20:59:08.8213149Z         and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2025-03-04T20:59:08.8213412Z         provided, the launcher will trigger an error: "error: unrecognized arguments:
2025-03-04T20:59:08.8213663Z         --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2025-03-04T20:59:08.8213824Z         including ``"--local-rank"`` should be sufficient.
2025-03-04T20:59:08.8213924Z     
2025-03-04T20:59:08.8214191Z     3. In your training program, you are supposed to call the following function
2025-03-04T20:59:08.8214450Z     at the beginning to start the distributed backend. It is strongly recommended
2025-03-04T20:59:08.8214680Z     that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2025-03-04T20:59:08.8214897Z     but ``env://`` is the one that is officially supported by this module.
2025-03-04T20:59:08.8214985Z     
2025-03-04T20:59:08.8215090Z     ::
2025-03-04T20:59:08.8215178Z     
2025-03-04T20:59:08.8215402Z         >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2025-03-04T20:59:08.8215546Z         >>>                                      init_method='env://')
2025-03-04T20:59:08.8215645Z     
2025-03-04T20:59:08.8215892Z     4. In your training program, you can either use regular distributed functions
2025-03-04T20:59:08.8216148Z     or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2025-03-04T20:59:08.8216365Z     training program uses GPUs for training and you would like to use
2025-03-04T20:59:08.8216578Z     :func:`torch.nn.parallel.DistributedDataParallel` module,
2025-03-04T20:59:08.8216694Z     here is how to configure it.
2025-03-04T20:59:08.8216792Z     
2025-03-04T20:59:08.8216883Z     ::
2025-03-04T20:59:08.8216980Z     
2025-03-04T20:59:08.8217210Z         >>> model = torch.nn.parallel.DistributedDataParallel(model,
2025-03-04T20:59:08.8217369Z         >>>                                                   device_ids=[args.local_rank],
2025-03-04T20:59:08.8217517Z         >>>                                                   output_device=args.local_rank)
2025-03-04T20:59:08.8217615Z     
2025-03-04T20:59:08.8217947Z     Please ensure that ``device_ids`` argument is set to be the only GPU device id
2025-03-04T20:59:08.8218207Z     that your code will be operating on. This is generally the local rank of the
2025-03-04T20:59:08.8218454Z     process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2025-03-04T20:59:08.8218692Z     and ``output_device`` needs to be ``args.local_rank`` in order to use this
2025-03-04T20:59:08.8218786Z     utility
2025-03-04T20:59:08.8218887Z     
2025-03-04T20:59:08.8219138Z     5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2025-03-04T20:59:08.8219375Z     ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2025-03-04T20:59:08.8219602Z     ``--use-env=True``. You must adjust the subprocess example above to replace
2025-03-04T20:59:08.8219850Z     ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2025-03-04T20:59:08.8220031Z     will not pass ``--local-rank`` when you specify this flag.
2025-03-04T20:59:08.8220155Z     
2025-03-04T20:59:08.8220255Z     .. warning::
2025-03-04T20:59:08.8220354Z     
2025-03-04T20:59:08.8220568Z         ``local_rank`` is NOT globally unique: it is only unique per process
2025-03-04T20:59:08.8220762Z         on a machine.  Thus, don't use it to decide if you should, e.g.,
2025-03-04T20:59:08.8220903Z         write to a networked filesystem.  See
2025-03-04T20:59:08.8221130Z         https://github.com/pytorch/pytorch/issues/12042 for an example of
2025-03-04T20:59:08.8221311Z         how things can go wrong if you don't do this correctly.
2025-03-04T20:59:08.8221400Z     
2025-03-04T20:59:08.8221502Z     
2025-03-04T20:59:08.8221589Z     
2025-03-04T20:59:08.8221690Z     
2025-03-04T20:59:08.8221951Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8222055Z     
2025-03-04T20:59:08.8222161Z       warnings.warn(msg)
2025-03-04T20:59:08.8222263Z     
2025-03-04T20:59:08.8222479Z --- Parse Warning: 44 / 116 ---
2025-03-04T20:59:08.8223543Z     /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.
2025-03-04T20:59:08.8223868Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8223974Z     
2025-03-04T20:59:08.8224225Z     Creates an :class:`ShardedTensor` from local shards and the global metadata.
2025-03-04T20:59:08.8224397Z     Needs to be called on all ranks in an SPMD fashion.
2025-03-04T20:59:08.8224487Z     
2025-03-04T20:59:08.8224593Z     Args:
2025-03-04T20:59:08.8224879Z         local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2025-03-04T20:59:08.8225077Z             of shards that represent the local shards on this rank.
2025-03-04T20:59:08.8225320Z         global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2025-03-04T20:59:08.8225460Z             shape of the overall sharded tensor.
2025-03-04T20:59:08.8225549Z     
2025-03-04T20:59:08.8225658Z     Keyword args:
2025-03-04T20:59:08.8225933Z         process_group (ProcessGroup, optional): The process group to work on. If None,
2025-03-04T20:59:08.8226078Z             the default process group will be used.
2025-03-04T20:59:08.8226262Z         init_rrefs (bool, optional): Whether or not to initialize
2025-03-04T20:59:08.8226493Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-04T20:59:08.8226696Z             Need to initialize the RPC Framework if specified as ``True``.
2025-03-04T20:59:08.8226843Z             Default: ``False``.
2025-03-04T20:59:08.8226933Z     
2025-03-04T20:59:08.8227036Z     Returns:
2025-03-04T20:59:08.8227200Z         A :class:`ShardedTensor` object handle on this rank
2025-03-04T20:59:08.8227306Z     
2025-03-04T20:59:08.8227395Z     
2025-03-04T20:59:08.8227501Z     Examples:
2025-03-04T20:59:08.8227765Z         Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2025-03-04T20:59:08.8227971Z         each shard have a (5, 5) local tensor, we can do it like below:
2025-03-04T20:59:08.8228061Z     
2025-03-04T20:59:08.8228168Z         on rank 0:
2025-03-04T20:59:08.8228299Z         >>> # xdoctest: +SKIP("not distributed")
2025-03-04T20:59:08.8228447Z         >>> local_shard_metadata = ShardMetadata(
2025-03-04T20:59:08.8228565Z         >>>     shard_offsets=[0, 0],
2025-03-04T20:59:08.8228677Z         >>>     shard_lengths=[5, 5],
2025-03-04T20:59:08.8228809Z         >>>     placement="rank:0/cuda:0"
2025-03-04T20:59:08.8228902Z         >>> )
2025-03-04T20:59:08.8229120Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-04T20:59:08.8229352Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-04T20:59:08.8229452Z     
2025-03-04T20:59:08.8229551Z         on rank 1:
2025-03-04T20:59:08.8229717Z         >>> # xdoctest: +SKIP("not distributed")
2025-03-04T20:59:08.8229851Z         >>> local_shard_metadata = ShardMetadata(
2025-03-04T20:59:08.8229979Z         >>>     shard_offsets=[5, 0],
2025-03-04T20:59:08.8230090Z         >>>     shard_lengths=[5, 5],
2025-03-04T20:59:08.8230219Z         >>>     placement="rank:1/cuda:1"
2025-03-04T20:59:08.8230310Z         >>> )
2025-03-04T20:59:08.8230547Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-03-04T20:59:08.8230825Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-03-04T20:59:08.8230949Z     
2025-03-04T20:59:08.8231216Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8231320Z     
2025-03-04T20:59:08.8231429Z       warnings.warn(msg)
2025-03-04T20:59:08.8231531Z     
2025-03-04T20:59:08.8231738Z --- Parse Warning: 45 / 116 ---
2025-03-04T20:59:08.8232846Z     /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=799.
2025-03-04T20:59:08.8233121Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8233220Z     
2025-03-04T20:59:08.8233525Z     Initialize a ShardedTensor given only one local tensor, global sharded tensor
2025-03-04T20:59:08.8233661Z     size and sharding spec on each rank.
2025-03-04T20:59:08.8233752Z     
2025-03-04T20:59:08.8233855Z     Args:
2025-03-04T20:59:08.8234173Z         local_tensor (Tensor): Single tensor of local shard stored in each rank.
2025-03-04T20:59:08.8234456Z         sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2025-03-04T20:59:08.8234637Z             The specification describing how to shard the Tensor.
2025-03-04T20:59:08.8234826Z         global_size (Sequence[int]): Size of the sharded tensor.
2025-03-04T20:59:08.8235089Z         process_group (ProcessGroup, optional): The process group to aggregate on.
2025-03-04T20:59:08.8235204Z             Default: None
2025-03-04T20:59:08.8235384Z         init_rrefs (bool, optional): Whether or not to initialize
2025-03-04T20:59:08.8235612Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-03-04T20:59:08.8235814Z             Need to initialize the RPC Framework if specified as ``True``.
2025-03-04T20:59:08.8235931Z             Default: ``False``.
2025-03-04T20:59:08.8236017Z     
2025-03-04T20:59:08.8236122Z     Returns:
2025-03-04T20:59:08.8236409Z         A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2025-03-04T20:59:08.8236550Z             tensor stored in the current rank.
2025-03-04T20:59:08.8236640Z     
2025-03-04T20:59:08.8236744Z     Examples:
2025-03-04T20:59:08.8236851Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8237006Z         >>> # All tensors below are of torch.int64 type.
2025-03-04T20:59:08.8237135Z         >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:59:08.8237331Z         >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2025-03-04T20:59:08.8237541Z         >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2025-03-04T20:59:08.8237656Z         >>> local_tensor
2025-03-04T20:59:08.8237766Z         tensor([[1, 2, 3, 4]]) # Rank 0
2025-03-04T20:59:08.8237876Z         tensor([[3, 4, 5, 6]]) # Rank 1
2025-03-04T20:59:08.8237991Z         >>> sharding_dim = 0
2025-03-04T20:59:08.8238124Z         >>> sharding_spec = ChunkShardingSpec(
2025-03-04T20:59:08.8238246Z                 dim=sharding_dim,
2025-03-04T20:59:08.8238351Z                 placements=[
2025-03-04T20:59:08.8238468Z                     "rank:0/cuda:0",
2025-03-04T20:59:08.8238600Z                     "rank:1/cuda:1",
2025-03-04T20:59:08.8238705Z                 ],
2025-03-04T20:59:08.8238796Z             )
2025-03-04T20:59:08.8238982Z         >>> st = ShardedTensor._init_from_local_tensor(
2025-03-04T20:59:08.8239116Z         ...     local_tensor, sharding_spec, [2, 4]
2025-03-04T20:59:08.8239219Z         ... )
2025-03-04T20:59:08.8239312Z         >>> st
2025-03-04T20:59:08.8239422Z         ShardedTensor(
2025-03-04T20:59:08.8239543Z             ShardedTensorMetadata(
2025-03-04T20:59:08.8239661Z                 shards_metadata=[
2025-03-04T20:59:08.8239944Z                     ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2025-03-04T20:59:08.8240227Z                     ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2025-03-04T20:59:08.8240321Z                 ],
2025-03-04T20:59:08.8240445Z                 size=torch.Size([2, 4])
2025-03-04T20:59:08.8240535Z         )
2025-03-04T20:59:08.8240651Z         >>> st.local_tensor()
2025-03-04T20:59:08.8240757Z         tensor([1, 2, 3, 4]) # Rank 0
2025-03-04T20:59:08.8240873Z         tensor([3, 4, 5, 6]) # Rank 1
2025-03-04T20:59:08.8240960Z     
2025-03-04T20:59:08.8241254Z     Warning: This API is experimental and subject to change. It lacks of a fully across
2025-03-04T20:59:08.8241605Z              rank validations, and we only validate the local shard on the current rank.
2025-03-04T20:59:08.8241893Z              We fully rely on the user to ensure local tensor is sharded based on the
2025-03-04T20:59:08.8242000Z              sharding spec.
2025-03-04T20:59:08.8242138Z     
2025-03-04T20:59:08.8242404Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8242503Z     
2025-03-04T20:59:08.8242609Z       warnings.warn(msg)
2025-03-04T20:59:08.8242713Z     
2025-03-04T20:59:08.8242937Z --- Parse Warning: 46 / 116 ---
2025-03-04T20:59:08.8244002Z     /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=1040.
2025-03-04T20:59:08.8244281Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8244384Z     
2025-03-04T20:59:08.8244652Z     Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2025-03-04T20:59:08.8244769Z     single local shard.
2025-03-04T20:59:08.8244859Z     
2025-03-04T20:59:08.8245102Z     If ``resharding_spec`` is same as the original one, this becomes a no-op.
2025-03-04T20:59:08.8245357Z     If only ``resharding_spec`` shares the same sharding dim with the original one,
2025-03-04T20:59:08.8245483Z     we swap local shards directly.
2025-03-04T20:59:08.8245783Z     For more generic cases, we merge different shards across different ranks and split
2025-03-04T20:59:08.8246054Z     the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2025-03-04T20:59:08.8246145Z     
2025-03-04T20:59:08.8246235Z     Args:
2025-03-04T20:59:08.8246546Z         resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2025-03-04T20:59:08.8246720Z             specification describing how the tensor is sharded.
2025-03-04T20:59:08.8246819Z     
2025-03-04T20:59:08.8246911Z     Returns:
2025-03-04T20:59:08.8247134Z         A :class:`ShardedTensor` object whose local shards are resharded.
2025-03-04T20:59:08.8247223Z     
2025-03-04T20:59:08.8247327Z     Examples:
2025-03-04T20:59:08.8247436Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8247575Z         >>> # We have 2 process groups, 2 ranks.
2025-03-04T20:59:08.8247760Z         >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2025-03-04T20:59:08.8247905Z         >>> tensor = torch.stack([tensor, tensor])
2025-03-04T20:59:08.8247998Z         >>> tensor
2025-03-04T20:59:08.8248140Z         tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2025-03-04T20:59:08.8248293Z         tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2025-03-04T20:59:08.8248430Z         tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2025-03-04T20:59:08.8248589Z         tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2025-03-04T20:59:08.8248704Z         >>> sharding_dim = 0
2025-03-04T20:59:08.8248823Z         >>> spec = ChunkShardingSpec(
2025-03-04T20:59:08.8248943Z                 dim=sharding_dim,
2025-03-04T20:59:08.8249045Z                 placements=[
2025-03-04T20:59:08.8249163Z                     "rank:0/cuda:0",
2025-03-04T20:59:08.8249265Z                     "rank:1/cuda:1",
2025-03-04T20:59:08.8249376Z                     "rank:2/cuda:2",
2025-03-04T20:59:08.8249478Z                     "rank:3/cuda:3",
2025-03-04T20:59:08.8249582Z                 ],
2025-03-04T20:59:08.8249675Z             )
2025-03-04T20:59:08.8249797Z         >>> current_offsets = [0] * 2
2025-03-04T20:59:08.8249916Z         >>> current_offsets[0] = rank * 2
2025-03-04T20:59:08.8250053Z         >>> shard_metadata = ShardMetadata(
2025-03-04T20:59:08.8250213Z                 shard_offsets=copy.deepcopy(current_offsets),
2025-03-04T20:59:08.8250344Z                 shard_sizes=tensor.size(),
2025-03-04T20:59:08.8250478Z                 placement=spec.placements[rank],
2025-03-04T20:59:08.8250580Z             )
2025-03-04T20:59:08.8250684Z         >>> local_shards = [
2025-03-04T20:59:08.8250779Z                 Shard(
2025-03-04T20:59:08.8250896Z                     tensor=tensor,
2025-03-04T20:59:08.8251017Z                     metadata=shard_metadata,
2025-03-04T20:59:08.8251145Z                 )
2025-03-04T20:59:08.8251237Z             ]
2025-03-04T20:59:08.8251493Z         >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2025-03-04T20:59:08.8251604Z         >>> sharding_dim = 1
2025-03-04T20:59:08.8251754Z         >>> resharding_spec = ChunkShardingSpec(
2025-03-04T20:59:08.8251867Z                 dim=sharding_dim,
2025-03-04T20:59:08.8251985Z                 placements=[
2025-03-04T20:59:08.8252090Z                     "rank:0/cuda:0",
2025-03-04T20:59:08.8252205Z                     "rank:1/cuda:1",
2025-03-04T20:59:08.8252307Z                     "rank:2/cuda:2",
2025-03-04T20:59:08.8252425Z                     "rank:3/cuda:3",
2025-03-04T20:59:08.8252518Z                 ],
2025-03-04T20:59:08.8252619Z             )
2025-03-04T20:59:08.8252735Z         >>> st.reshard(resharding_spec)
2025-03-04T20:59:08.8252870Z         >>> tensor = st.local_shards()[0].tensor
2025-03-04T20:59:08.8252964Z         >>> tensor
2025-03-04T20:59:08.8253127Z         tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2025-03-04T20:59:08.8253273Z         tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2025-03-04T20:59:08.8253432Z         tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2025-03-04T20:59:08.8253671Z         tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2025-03-04T20:59:08.8253773Z     
2025-03-04T20:59:08.8254038Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8254140Z     
2025-03-04T20:59:08.8254247Z       warnings.warn(msg)
2025-03-04T20:59:08.8254349Z     
2025-03-04T20:59:08.8254554Z --- Parse Warning: 47 / 116 ---
2025-03-04T20:59:08.8255572Z     /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.
2025-03-04T20:59:08.8255847Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8255952Z     
2025-03-04T20:59:08.8256182Z     Representation of a sharding plan, describes how to shard a module
2025-03-04T20:59:08.8256478Z     across hosts. `plan` is used to shard module parameters according to the spec provided,
2025-03-04T20:59:08.8256776Z     `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2025-03-04T20:59:08.8257074Z     layout of a module with a spec, and when to convert back to data parallel fashion.
2025-03-04T20:59:08.8257165Z     
2025-03-04T20:59:08.8257272Z     Args:
2025-03-04T20:59:08.8257590Z         plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2025-03-04T20:59:08.8257862Z               :class:`torch.distributed._shard.sharder.Sharder`]):
2025-03-04T20:59:08.8258147Z             a dict describes how to shard a module, there're currently two ways to shard a module:
2025-03-04T20:59:08.8258427Z                 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2025-03-04T20:59:08.8258564Z                    a parameter to a `ShardingSpec`.
2025-03-04T20:59:08.8258845Z                 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2025-03-04T20:59:08.8258963Z                    to a `Sharder` object.
2025-03-04T20:59:08.8259315Z         output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2025-03-04T20:59:08.8259587Z             a dict specifies the layout of a module's output which produces a ShardedTensor,
2025-03-04T20:59:08.8259847Z             keyed by the name of module to ShardingSpec("" in key means the root module).
2025-03-04T20:59:08.8259954Z             Default: `None`
2025-03-04T20:59:08.8260230Z         return_local_tensor (List[str], optional): a list of string, each element enables
2025-03-04T20:59:08.8260480Z             a module's sharded output to be returned as a Tensor from its local shards to
2025-03-04T20:59:08.8260782Z             ensure further processing in a data parallel fashion. ("" in list means the
2025-03-04T20:59:08.8260888Z             root module).
2025-03-04T20:59:08.8261003Z             Default: None
2025-03-04T20:59:08.8261101Z     Example:
2025-03-04T20:59:08.8261406Z       Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2025-03-04T20:59:08.8261703Z       want to convert the output of the second linear layer back to DDP, we can do it as follows:
2025-03-04T20:59:08.8261803Z     
2025-03-04T20:59:08.8261984Z         >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2025-03-04T20:59:08.8262113Z         >>> class MyModule(nn.Module):
2025-03-04T20:59:08.8262234Z         >>>     def __init__(self) -> None:
2025-03-04T20:59:08.8262363Z         >>>        super().__init__()
2025-03-04T20:59:08.8262476Z         >>>        self.fc1 = nn.Linear()
2025-03-04T20:59:08.8262602Z         >>>        self.gelu = nn.GELU()
2025-03-04T20:59:08.8262713Z         >>>        self.fc2 = nn.Linear()
2025-03-04T20:59:08.8262840Z         >>>        self.relu = nn.Linear()
2025-03-04T20:59:08.8262931Z         >>>
2025-03-04T20:59:08.8263045Z         >>>     def forward(self, input):
2025-03-04T20:59:08.8263266Z         >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2025-03-04T20:59:08.8263354Z     
2025-03-04T20:59:08.8263453Z     
2025-03-04T20:59:08.8263593Z         >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2025-03-04T20:59:08.8263723Z         >>> sharding_plan = ShardingPlan(
2025-03-04T20:59:08.8263819Z         >>>    plan={
2025-03-04T20:59:08.8263943Z         >>>        "fc1.weight": spec1,
2025-03-04T20:59:08.8264053Z         >>>        "fc2.weight": spec2
2025-03-04T20:59:08.8264155Z         >>>    },
2025-03-04T20:59:08.8264259Z         >>>    output_plan={
2025-03-04T20:59:08.8264378Z         >>>        "fc2": output_spec
2025-03-04T20:59:08.8264467Z         >>>    },
2025-03-04T20:59:08.8264596Z         >>>    return_local_tensor=["fc2"]
2025-03-04T20:59:08.8264688Z         >>> )
2025-03-04T20:59:08.8264787Z     
2025-03-04T20:59:08.8265052Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8265152Z     
2025-03-04T20:59:08.8265260Z       warnings.warn(msg)
2025-03-04T20:59:08.8265363Z     
2025-03-04T20:59:08.8265568Z --- Parse Warning: 48 / 116 ---
2025-03-04T20:59:08.8266762Z     /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.
2025-03-04T20:59:08.8267038Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8267143Z     
2025-03-04T20:59:08.8267263Z     Run post-localSGD algorithm.
2025-03-04T20:59:08.8267367Z     
2025-03-04T20:59:08.8267614Z     This DDP communication hook is used for running post-localSGD algorithm,
2025-03-04T20:59:08.8267799Z     by combining with a model averaging component (e.g.,
2025-03-04T20:59:08.8268139Z     :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2025-03-04T20:59:08.8268277Z     that runs after the optimizer step.
2025-03-04T20:59:08.8268365Z     
2025-03-04T20:59:08.8268467Z     Args:
2025-03-04T20:59:08.8268700Z         state (PostLocalSGDState): State information to run post-localSGD.
2025-03-04T20:59:08.8268997Z             Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2025-03-04T20:59:08.8269428Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-04T20:59:08.8269697Z             Note that since DDP comm hook only supports single process single device mode,
2025-03-04T20:59:08.8269861Z             only exactly one tensor is stored in this bucket.
2025-03-04T20:59:08.8269962Z     
2025-03-04T20:59:08.8270057Z     Returns:
2025-03-04T20:59:08.8270351Z         Future handler of the communication, which updates the gradients in place.
2025-03-04T20:59:08.8270441Z     
2025-03-04T20:59:08.8270559Z     Example::
2025-03-04T20:59:08.8270666Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8270930Z         >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2025-03-04T20:59:08.8271073Z                                   start_localSGD_iter=10)
2025-03-04T20:59:08.8271255Z         >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:59:08.8271614Z         >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2025-03-04T20:59:08.8271982Z         >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2025-03-04T20:59:08.8272072Z     
2025-03-04T20:59:08.8272352Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8272442Z     
2025-03-04T20:59:08.8272551Z       warnings.warn(msg)
2025-03-04T20:59:08.8272656Z     
2025-03-04T20:59:08.8272853Z --- Parse Warning: 49 / 116 ---
2025-03-04T20:59:08.8274175Z     /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.
2025-03-04T20:59:08.8274563Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8274654Z     
2025-03-04T20:59:08.8274778Z     Implement PowerSGD algorithm.
2025-03-04T20:59:08.8274881Z     
2025-03-04T20:59:08.8275117Z     This DDP communication hook implements PowerSGD gradient compression
2025-03-04T20:59:08.8275383Z     algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2025-03-04T20:59:08.8275637Z     Once gradient tensors are aggregated across all workers, this hook applies
2025-03-04T20:59:08.8275766Z     compression as follows:
2025-03-04T20:59:08.8275856Z     
2025-03-04T20:59:08.8276318Z     1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2025-03-04T20:59:08.8276411Z     
2025-03-04T20:59:08.8276853Z         1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2025-03-04T20:59:08.8276981Z     
2025-03-04T20:59:08.8277443Z         1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2025-03-04T20:59:08.8277564Z     
2025-03-04T20:59:08.8277695Z     2. Handles uncompressed tensors:
2025-03-04T20:59:08.8277785Z     
2025-03-04T20:59:08.8278340Z         2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2025-03-04T20:59:08.8278429Z     
2025-03-04T20:59:08.8278791Z         2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2025-03-04T20:59:08.8278879Z     
2025-03-04T20:59:08.8279134Z     3. Handles the tensors that should be compressed by PowerSGD compression:
2025-03-04T20:59:08.8279223Z     
2025-03-04T20:59:08.8279483Z         3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2025-03-04T20:59:08.8279804Z         such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2025-03-04T20:59:08.8279902Z     
2025-03-04T20:59:08.8280060Z         3.2. Computes each P in Ps, which is equal to MQ;
2025-03-04T20:59:08.8280160Z     
2025-03-04T20:59:08.8280278Z         3.3. Allreduces Ps as a batch;
2025-03-04T20:59:08.8280378Z     
2025-03-04T20:59:08.8280501Z         3.4. Orthogonalizes each P in Ps;
2025-03-04T20:59:08.8280601Z     
2025-03-04T20:59:08.8280806Z         3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2025-03-04T20:59:08.8280907Z     
2025-03-04T20:59:08.8281059Z         3.6. Allreduces Qs as a batch;
2025-03-04T20:59:08.8281165Z     
2025-03-04T20:59:08.8281470Z         3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2025-03-04T20:59:08.8281570Z     
2025-03-04T20:59:08.8281989Z     Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2025-03-04T20:59:08.8282292Z     This not only gives the user more control over the tradeoff between speedup and accuracy,
2025-03-04T20:59:08.8282736Z     but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2025-03-04T20:59:08.8282834Z     
2025-03-04T20:59:08.8282925Z     Args:
2025-03-04T20:59:08.8283376Z         state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2025-03-04T20:59:08.8283745Z             To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2025-03-04T20:59:08.8283880Z             and ``min_compression_rate``.
2025-03-04T20:59:08.8284306Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-03-04T20:59:08.8284603Z             Note that since DDP comm hook only supports single process single device mode,
2025-03-04T20:59:08.8284768Z             only exactly one tensor is stored in this bucket.
2025-03-04T20:59:08.8284869Z     
2025-03-04T20:59:08.8284962Z     Returns:
2025-03-04T20:59:08.8285229Z         Future handler of the communication, which updates the gradients in place.
2025-03-04T20:59:08.8285318Z     
2025-03-04T20:59:08.8285431Z     Example::
2025-03-04T20:59:08.8285539Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8285836Z         >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2025-03-04T20:59:08.8286006Z                                   start_powerSGD_iter=10, min_compression_rate=0.5)
2025-03-04T20:59:08.8286190Z         >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2025-03-04T20:59:08.8286277Z     
2025-03-04T20:59:08.8286552Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8286644Z     
2025-03-04T20:59:08.8286762Z       warnings.warn(msg)
2025-03-04T20:59:08.8286851Z     
2025-03-04T20:59:08.8287097Z --- Parse Warning: 50 / 116 ---
2025-03-04T20:59:08.8288256Z     /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.
2025-03-04T20:59:08.8288543Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8288632Z     
2025-03-04T20:59:08.8288845Z     Averages parameters periodically after the warm-up stage.
2025-03-04T20:59:08.8288934Z     
2025-03-04T20:59:08.8289201Z     This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-04T20:59:08.8289418Z     by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2025-03-04T20:59:08.8289663Z     using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2025-03-04T20:59:08.8289769Z     
2025-03-04T20:59:08.8289861Z     Args:
2025-03-04T20:59:08.8290052Z         period (int): The number of steps per model averaging.
2025-03-04T20:59:08.8290329Z                       Usually the period should be greater than ``1`` to reduce the communication cost.
2025-03-04T20:59:08.8290481Z                       Otherwise, only DDP needs to be used.
2025-03-04T20:59:08.8290701Z         warmup_steps (int): The number of warm-up steps. During this stage,
2025-03-04T20:59:08.8290843Z                             model averaging is skipped.
2025-03-04T20:59:08.8291040Z         process_group: The process group to be used for all-reduce.
2025-03-04T20:59:08.8291231Z                        If ``None``, the default process group, which
2025-03-04T20:59:08.8291431Z                        is created by :func:`torch.distributed.init_process_group`,
2025-03-04T20:59:08.8291574Z                        will be used. (default: ``None``)
2025-03-04T20:59:08.8291665Z     
2025-03-04T20:59:08.8291777Z     Example::
2025-03-04T20:59:08.8291867Z     
2025-03-04T20:59:08.8292022Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8292125Z         >>> import torch
2025-03-04T20:59:08.8292269Z         >>> import torch.distributed as dist
2025-03-04T20:59:08.8292598Z         >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2025-03-04T20:59:08.8292894Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-04T20:59:08.8293007Z         >>> import torch.nn as nn
2025-03-04T20:59:08.8293116Z         >>>
2025-03-04T20:59:08.8293305Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-04T20:59:08.8293440Z         >>> torch.cuda.set_device(rank)
2025-03-04T20:59:08.8293584Z         >>> module = nn.Linear(1, 1, bias=False).cuda()
2025-03-04T20:59:08.8293761Z         >>> model = nn.parallel.DistributedDataParallel(
2025-03-04T20:59:08.8293942Z         >>>    module, device_ids=[rank], output_device=rank
2025-03-04T20:59:08.8294050Z         >>> )
2025-03-04T20:59:08.8294208Z         >>> # Register a post-localSGD communication hook.
2025-03-04T20:59:08.8294529Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-04T20:59:08.8294707Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:59:08.8294815Z         >>>
2025-03-04T20:59:08.8295092Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-04T20:59:08.8295271Z         >>> # After 100 steps, run model averaging every 4 steps.
2025-03-04T20:59:08.8295599Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-04T20:59:08.8295868Z         >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-04T20:59:08.8295983Z         >>> for step in range(0, 200):
2025-03-04T20:59:08.8296108Z         >>>    optimizer.zero_grad()
2025-03-04T20:59:08.8296230Z         >>>    loss = loss_fn(output, labels)
2025-03-04T20:59:08.8296389Z         >>>    loss.backward()
2025-03-04T20:59:08.8296498Z         >>>    optimizer.step()
2025-03-04T20:59:08.8296715Z         >>>    # Will average model parameters globally every 4 steps. Thus,
2025-03-04T20:59:08.8296951Z         >>>    # inter-node communication only occurs every 4 iterations after
2025-03-04T20:59:08.8297098Z         >>>    # the initial ``warmup_steps`` period.
2025-03-04T20:59:08.8297268Z         >>>    averager.average_parameters(model.parameters())
2025-03-04T20:59:08.8297368Z     
2025-03-04T20:59:08.8297629Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8297804Z     
2025-03-04T20:59:08.8297913Z       warnings.warn(msg)
2025-03-04T20:59:08.8298015Z     
2025-03-04T20:59:08.8298220Z --- Parse Warning: 51 / 116 ---
2025-03-04T20:59:08.8299460Z     /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.
2025-03-04T20:59:08.8299738Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8299839Z     
2025-03-04T20:59:08.8300184Z     Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2025-03-04T20:59:08.8300288Z     
2025-03-04T20:59:08.8300608Z     Process groups of different sizes are organized in a hierarchy, and they average parameters
2025-03-04T20:59:08.8300868Z     by using different periods concurrently after the warm-up stage.
2025-03-04T20:59:08.8301283Z     This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2025-03-04T20:59:08.8301636Z     that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2025-03-04T20:59:08.8301953Z     a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2025-03-04T20:59:08.8302327Z     level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2025-03-04T20:59:08.8302638Z     Similarly, the process groups within this class do not have such an intra-machine process
2025-03-04T20:59:08.8302935Z     subgroup, which should be embedded by the post-local SGD communication hook instead.
2025-03-04T20:59:08.8303025Z     
2025-03-04T20:59:08.8303128Z     Args:
2025-03-04T20:59:08.8303401Z         period_group_size_dict: An ordered dict mapping keys of model averaging period to
2025-03-04T20:59:08.8303630Z                                 process group size, used for initializing process groups of
2025-03-04T20:59:08.8303868Z                                 different sizes in a hierarchy to average parameters concurrently.
2025-03-04T20:59:08.8304133Z                                 Particularly, at each iteration, there will be at most a single
2025-03-04T20:59:08.8304377Z                                 process group that runs averaging -- the period of such group should
2025-03-04T20:59:08.8304611Z                                 have the largest period which the current step can be divided by.
2025-03-04T20:59:08.8304789Z                                 For example, if the dict has three keys: 2, 4, and 8,
2025-03-04T20:59:08.8305017Z                                 then this means totally three process groups will be created to
2025-03-04T20:59:08.8305237Z                                 average parameters every 2, 4, and 8 iterations, respectively.
2025-03-04T20:59:08.8305450Z                                 At the 4th iteration, only the second process group will run
2025-03-04T20:59:08.8305636Z                                 averaging, because the first process group should be a
2025-03-04T20:59:08.8305880Z                                 subset of the second process group, and no need to execute the first
2025-03-04T20:59:08.8306016Z                                 process group redundantly.
2025-03-04T20:59:08.8306264Z                                 On the other hand, the third process group can only be triggered
2025-03-04T20:59:08.8306520Z                                 every 8 iterations, so it will not be triggered at the 4th iteration.
2025-03-04T20:59:08.8306845Z         warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2025-03-04T20:59:08.8307298Z         process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2025-03-04T20:59:08.8307494Z                                                 If ``None``, the default process group, which is created
2025-03-04T20:59:08.8307713Z                                                 by :func:`torch.distributed.init_process_group`, will be used.
2025-03-04T20:59:08.8307857Z                                                 (default: ``None``)
2025-03-04T20:59:08.8307947Z     
2025-03-04T20:59:08.8308059Z     Example::
2025-03-04T20:59:08.8308187Z         >>> # xdoctest: +SKIP('undefined rank')
2025-03-04T20:59:08.8308328Z         >>> from collections import OrderedDict
2025-03-04T20:59:08.8308431Z         >>> import torch
2025-03-04T20:59:08.8308571Z         >>> import torch.distributed as dist
2025-03-04T20:59:08.8308854Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-04T20:59:08.8308980Z         >>>     PostLocalSGDState,
2025-03-04T20:59:08.8309092Z         >>>     post_localSGD_hook,
2025-03-04T20:59:08.8309194Z         >>> )
2025-03-04T20:59:08.8309607Z         >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2025-03-04T20:59:08.8309733Z         >>> import torch.nn as nn
2025-03-04T20:59:08.8309824Z         >>>
2025-03-04T20:59:08.8310023Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-03-04T20:59:08.8310144Z         >>> torch.cuda.set_device(rank)
2025-03-04T20:59:08.8310303Z         >>> module = nn.Linear(1, 1, bias=False).to(rank)
2025-03-04T20:59:08.8310465Z         >>> model = nn.parallel.DistributedDataParallel(
2025-03-04T20:59:08.8310626Z         >>>    module, device_ids=[rank], output_device=rank
2025-03-04T20:59:08.8310718Z         >>> )
2025-03-04T20:59:08.8310886Z         >>> # Register a post-localSGD communication hook.
2025-03-04T20:59:08.8311174Z         >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2025-03-04T20:59:08.8311313Z         >>> subgroup, _ = dist.new_subgroups()
2025-03-04T20:59:08.8311634Z         >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2025-03-04T20:59:08.8311817Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:59:08.8311908Z         >>>
2025-03-04T20:59:08.8312210Z         >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2025-03-04T20:59:08.8312368Z         >>> # the 16 processes every 16 iterations.
2025-03-04T20:59:08.8312576Z         >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2025-03-04T20:59:08.8312818Z         >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2025-03-04T20:59:08.8313153Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-04T20:59:08.8313431Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-03-04T20:59:08.8313608Z         >>> # After 100 steps, run model averaging at two levels.
2025-03-04T20:59:08.8313726Z         >>> for step in range(0, 200):
2025-03-04T20:59:08.8313851Z         >>>    optimizer.zero_grad()
2025-03-04T20:59:08.8313973Z         >>>    loss = loss_fn(output, labels)
2025-03-04T20:59:08.8314091Z         >>>    loss.backward()
2025-03-04T20:59:08.8314200Z         >>>    optimizer.step()
2025-03-04T20:59:08.8314376Z         >>>    # Average parameters after ``optimizer.step()``.
2025-03-04T20:59:08.8314697Z         >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2025-03-04T20:59:08.8314880Z         >>>    averager.average_parameters(model.parameters())
2025-03-04T20:59:08.8314992Z     
2025-03-04T20:59:08.8315105Z     .. warning ::
2025-03-04T20:59:08.8315367Z         The last group size in the dict must be the size of the provided ``process_group``,
2025-03-04T20:59:08.8315619Z         which indicates model averaging at the highest level of the hierarchy.
2025-03-04T20:59:08.8315934Z         If ``process_group`` is not provided, then the last group size should be equal to the world size.
2025-03-04T20:59:08.8316035Z     
2025-03-04T20:59:08.8316131Z     .. warning ::
2025-03-04T20:59:08.8316387Z         `HierarchicalModelAverager` is experimental and subject to change.
2025-03-04T20:59:08.8316478Z     
2025-03-04T20:59:08.8316748Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8316837Z     
2025-03-04T20:59:08.8316954Z       warnings.warn(msg)
2025-03-04T20:59:08.8317042Z     
2025-03-04T20:59:08.8317261Z --- Parse Warning: 52 / 116 ---
2025-03-04T20:59:08.8318352Z     /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.
2025-03-04T20:59:08.8318641Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8318731Z     
2025-03-04T20:59:08.8319080Z     StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2025-03-04T20:59:08.8319337Z     on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2025-03-04T20:59:08.8319440Z     
2025-03-04T20:59:08.8319616Z     . N.B. Intended to be used with DynamicMetaLoadPlanner
2025-03-04T20:59:08.8319717Z     
2025-03-04T20:59:08.8319816Z     .. warning::
2025-03-04T20:59:08.8320012Z         Current implementation only supports loading Tensors.
2025-03-04T20:59:08.8320101Z     
2025-03-04T20:59:08.8320228Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8320349Z     >>> sd = {"mode": model}
2025-03-04T20:59:08.8320446Z     >>> dcp.load(
2025-03-04T20:59:08.8320551Z     >>>    sd,
2025-03-04T20:59:08.8320716Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-04T20:59:08.8320863Z     >>>    planner=DynamicMetaLoadPlanner(),
2025-03-04T20:59:08.8320989Z     >>>    checkpoint_id="path_to_model.pt"
2025-03-04T20:59:08.8321094Z     >>> )
2025-03-04T20:59:08.8321188Z     
2025-03-04T20:59:08.8321466Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8321554Z     
2025-03-04T20:59:08.8321673Z       warnings.warn(msg)
2025-03-04T20:59:08.8321788Z     
2025-03-04T20:59:08.8321994Z --- Parse Warning: 53 / 116 ---
2025-03-04T20:59:08.8323120Z     /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.
2025-03-04T20:59:08.8323444Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8323535Z     
2025-03-04T20:59:08.8323927Z     Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2025-03-04T20:59:08.8324263Z     avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2025-03-04T20:59:08.8324400Z     metadata file, like Torch Save files.
2025-03-04T20:59:08.8324489Z     
2025-03-04T20:59:08.8324692Z     . N.B. Intended to be used with BroadcastingTorchSaveReader
2025-03-04T20:59:08.8324782Z     
2025-03-04T20:59:08.8324894Z     .. warning::
2025-03-04T20:59:08.8325078Z         Current implementation only supports loading Tensors.
2025-03-04T20:59:08.8325214Z     
2025-03-04T20:59:08.8325563Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8325721Z     >>> sd = {"mode": model}
2025-03-04T20:59:08.8325894Z     >>> dcp.load(
2025-03-04T20:59:08.8326046Z     >>>    sd,
2025-03-04T20:59:08.8326291Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-03-04T20:59:08.8326450Z     >>>    planner=DynamicMetaLoadPlanner(),
2025-03-04T20:59:08.8326717Z     >>>    checkpoint_id="path_to_model.pt"
2025-03-04T20:59:08.8326843Z     >>> )
2025-03-04T20:59:08.8327006Z     
2025-03-04T20:59:08.8327302Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8327522Z     
2025-03-04T20:59:08.8327689Z       warnings.warn(msg)
2025-03-04T20:59:08.8327874Z     
2025-03-04T20:59:08.8328115Z --- Parse Warning: 54 / 116 ---
2025-03-04T20:59:08.8329261Z     /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.
2025-03-04T20:59:08.8329579Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8329753Z     
2025-03-04T20:59:08.8330020Z     Load a state_dict in conjunction with FSDP sharded optimizer state.
2025-03-04T20:59:08.8330187Z     
2025-03-04T20:59:08.8330393Z     This is the current recommended way to checkpoint FSDP.
2025-03-04T20:59:08.8330583Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8330758Z     >>> import torch.distributed.checkpoint as dist_cp
2025-03-04T20:59:08.8330999Z     >>> # Save
2025-03-04T20:59:08.8331141Z     >>> model: torch.nn.Model
2025-03-04T20:59:08.8331354Z     >>> optim_params = model.parameters()
2025-03-04T20:59:08.8331541Z     >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2025-03-04T20:59:08.8331667Z     >>> # Save
2025-03-04T20:59:08.8331979Z     >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2025-03-04T20:59:08.8332175Z     >>>     state_dict = {
2025-03-04T20:59:08.8332372Z     >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2025-03-04T20:59:08.8332533Z     >>>         "model": model.state_dict()
2025-03-04T20:59:08.8332757Z     >>>     }
2025-03-04T20:59:08.8332883Z     >>>     dist_cp.save_state_dict(
2025-03-04T20:59:08.8333115Z     >>>         state_dict=optim_state,
2025-03-04T20:59:08.8333335Z     >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2025-03-04T20:59:08.8333560Z     >>>         planner=dist_cp.DefaultSavePlanner(),
2025-03-04T20:59:08.8333686Z     >>>     )
2025-03-04T20:59:08.8333833Z     >>>
2025-03-04T20:59:08.8333982Z     >>> # Load
2025-03-04T20:59:08.8334316Z     >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2025-03-04T20:59:08.8334534Z     >>>     model_state_dict = model_tp.state_dict()
2025-03-04T20:59:08.8334780Z     >>>     checkpoint = {
2025-03-04T20:59:08.8334939Z     >>>         "model": model_state_dict
2025-03-04T20:59:08.8335122Z     >>>     }
2025-03-04T20:59:08.8335282Z     >>>     dist_cp.load_state_dict(
2025-03-04T20:59:08.8335483Z     >>>         state_dict=checkpoint,
2025-03-04T20:59:08.8335718Z     >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2025-03-04T20:59:08.8335935Z     >>>         planner=dist_cp.DefaultLoadPlanner(),
2025-03-04T20:59:08.8336040Z     >>>     )
2025-03-04T20:59:08.8336327Z     >>>     model.load_state_dict(checkpoint["model_state"])
2025-03-04T20:59:08.8336448Z     >>>
2025-03-04T20:59:08.8336706Z     >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2025-03-04T20:59:08.8336902Z     >>>         model_state_dict,
2025-03-04T20:59:08.8337077Z     >>>         optimizer_key="optimizer",
2025-03-04T20:59:08.8337338Z     >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2025-03-04T20:59:08.8337536Z     >>>     )
2025-03-04T20:59:08.8337659Z     >>>
2025-03-04T20:59:08.8338020Z     >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2025-03-04T20:59:08.8338235Z     >>>        model, optim, optim_state["optimizer"]
2025-03-04T20:59:08.8338433Z     >>>     )
2025-03-04T20:59:08.8338570Z     >>>
2025-03-04T20:59:08.8338805Z     >>>     optim.load_state_dict(flattened_osd)
2025-03-04T20:59:08.8338928Z     
2025-03-04T20:59:08.8339268Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8339381Z     
2025-03-04T20:59:08.8339610Z       warnings.warn(msg)
2025-03-04T20:59:08.8339731Z     
2025-03-04T20:59:08.8340033Z --- Parse Warning: 55 / 116 ---
2025-03-04T20:59:08.8341041Z     /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=113.
2025-03-04T20:59:08.8341382Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8341540Z     
2025-03-04T20:59:08.8341927Z     Abstract class defining the protocol used by save_state_dict to plan the save process.
2025-03-04T20:59:08.8342111Z     
2025-03-04T20:59:08.8342500Z     SavePlanners are stateful objects that can be used to customize the whole save process.
2025-03-04T20:59:08.8342629Z     
2025-03-04T20:59:08.8343009Z     SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-04T20:59:08.8343182Z     will be visible to the whole process.
2025-03-04T20:59:08.8343346Z     
2025-03-04T20:59:08.8343693Z     A planner subclass can expect the following sequence of calls during save_state_dict:
2025-03-04T20:59:08.8343863Z     
2025-03-04T20:59:08.8344004Z     1) set_up_planner - called on all ranks.
2025-03-04T20:59:08.8344262Z         Signals the start of a checkpoint save.
2025-03-04T20:59:08.8344389Z     
2025-03-04T20:59:08.8344679Z     2) create_local_plan - called on all ranks.
2025-03-04T20:59:08.8345064Z         Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2025-03-04T20:59:08.8345217Z     
2025-03-04T20:59:08.8345502Z     3) create_global_plan - called on the coordinator rank only.
2025-03-04T20:59:08.8345803Z         Takes the SavePlan from all ranks and make any global decision.
2025-03-04T20:59:08.8345922Z     
2025-03-04T20:59:08.8346128Z     4) finish_plan - called on all ranks.
2025-03-04T20:59:08.8346386Z         This gives each rank a chance to adjust to global planning decisions.
2025-03-04T20:59:08.8346562Z     
2025-03-04T20:59:08.8346771Z     5) resolve_data - called multiple times on each rank
2025-03-04T20:59:08.8347134Z         Lookups a value on the `state_dict` for the storage layer to write.
2025-03-04T20:59:08.8347255Z     
2025-03-04T20:59:08.8347650Z     Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2025-03-04T20:59:08.8347894Z     most changes can be expressed by changes in a single method.
2025-03-04T20:59:08.8348103Z     
2025-03-04T20:59:08.8348277Z     There are 3 usual patterns of extension:
2025-03-04T20:59:08.8348443Z     
2025-03-04T20:59:08.8348737Z     Rewriting state_dict. This is the simplest way to extend the save process as it
2025-03-04T20:59:08.8349029Z     doesn't requite understanding the intrincacies of how SavePlan works:
2025-03-04T20:59:08.8349178Z     
2025-03-04T20:59:08.8349400Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8349575Z     >>> class RenamePlanner(DefaultSavePlanner):
2025-03-04T20:59:08.8349757Z     >>>     def set_up_planner(
2025-03-04T20:59:08.8349883Z     >>>         self,
2025-03-04T20:59:08.8350018Z     >>>         state_dict: STATE_DICT_TYPE,
2025-03-04T20:59:08.8350284Z     >>>         storage_meta: Optional[StorageMeta],
2025-03-04T20:59:08.8350429Z     >>>         is_coordinator: bool,
2025-03-04T20:59:08.8350599Z     >>>     ) -> None:
2025-03-04T20:59:08.8350749Z     >>> # prefix all keys with `foo_``
2025-03-04T20:59:08.8351106Z     >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2025-03-04T20:59:08.8351292Z     
2025-03-04T20:59:08.8351820Z     Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2025-03-04T20:59:08.8351943Z     
2025-03-04T20:59:08.8352143Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8352313Z     >>> class FP16Planner(DefaultSavePlanner):
2025-03-04T20:59:08.8352524Z     >>>     def create_local_plan(self):
2025-03-04T20:59:08.8352707Z     >>>         plan = super().create_local_plan()
2025-03-04T20:59:08.8352891Z     >>>         for p in plan:
2025-03-04T20:59:08.8353050Z     >>>             if p.tensor_data is not None:
2025-03-04T20:59:08.8353296Z     >>>                 p.tensor_data.properties.dtype = torch.float16
2025-03-04T20:59:08.8353418Z     >>>         return plan
2025-03-04T20:59:08.8353634Z     >>>
2025-03-04T20:59:08.8353792Z     >>>     def resolve_data(self, write_item):
2025-03-04T20:59:08.8354006Z     >>>         item = super().resolve_data(write_item)
2025-03-04T20:59:08.8354323Z     >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2025-03-04T20:59:08.8354474Z     
2025-03-04T20:59:08.8354887Z     Using the global planning step to make central decisions that can't be made individually by each rank
2025-03-04T20:59:08.8355070Z     
2025-03-04T20:59:08.8355225Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8355423Z     >>> from itertools import zip_longest
2025-03-04T20:59:08.8355583Z     >>> from dataclasses import replace
2025-03-04T20:59:08.8355950Z     >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2025-03-04T20:59:08.8356287Z     >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
2025-03-04T20:59:08.8356504Z     >>> # This sample doesn't handle ShardedTensors
2025-03-04T20:59:08.8356680Z     >>>     def create_global_plan(self, all_plans):
2025-03-04T20:59:08.8356924Z     >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2025-03-04T20:59:08.8357047Z     >>>         items_per_rank = [
2025-03-04T20:59:08.8357323Z     >>>             [item for item in items if item is not None]
2025-03-04T20:59:08.8357524Z     >>>             for items in zip(*zip_longest(*iters), strict=True)
2025-03-04T20:59:08.8357699Z     >>>         ]
2025-03-04T20:59:08.8357834Z     >>>         all_plans = [
2025-03-04T20:59:08.8358018Z     >>>             replace(plan, items=items)
2025-03-04T20:59:08.8358274Z     >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2025-03-04T20:59:08.8358457Z     >>>         ]
2025-03-04T20:59:08.8358644Z     >>>         return super().create_global_plan(all_plans)
2025-03-04T20:59:08.8358814Z     
2025-03-04T20:59:08.8359120Z     Finally, some planners need to save additional metadata in the checkpoint, this is
2025-03-04T20:59:08.8359512Z     accomplished by having each rank contribute their data items in the local plan and
2025-03-04T20:59:08.8359689Z     the global planner aggregate them:
2025-03-04T20:59:08.8359864Z     
2025-03-04T20:59:08.8360022Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8360326Z     >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2025-03-04T20:59:08.8360480Z     >>>     def create_local_plan(self) -> SavePlan:
2025-03-04T20:59:08.8360726Z     >>>         plan = super().create_local_plan()
2025-03-04T20:59:08.8360938Z     >>>         return replace(plan, planner_data="per-rank-data")
2025-03-04T20:59:08.8361105Z     >>>
2025-03-04T20:59:08.8361449Z     >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2025-03-04T20:59:08.8361710Z     >>>         global_plan, metadata = super().create_global_plan(all_plans)
2025-03-04T20:59:08.8368966Z     >>>         merged_data = [p.planner_data for p in global_plan]
2025-03-04T20:59:08.8369222Z     >>>         metadata = replace(metadata, planner_data=merged_data)
2025-03-04T20:59:08.8369364Z     >>>         return global_plan, metadata
2025-03-04T20:59:08.8369547Z     
2025-03-04T20:59:08.8369827Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8369918Z     
2025-03-04T20:59:08.8370086Z       warnings.warn(msg)
2025-03-04T20:59:08.8370178Z     
2025-03-04T20:59:08.8370448Z --- Parse Warning: 56 / 116 ---
2025-03-04T20:59:08.8371430Z     /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=293.
2025-03-04T20:59:08.8371719Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8371810Z     
2025-03-04T20:59:08.8372121Z     Abstract class defining the protocol used by load_state_dict to plan the load process.
2025-03-04T20:59:08.8372214Z     
2025-03-04T20:59:08.8372527Z     LoadPlanner are stateful objects that can be used to customize the whole load process.
2025-03-04T20:59:08.8372620Z     
2025-03-04T20:59:08.8372921Z     LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-03-04T20:59:08.8373048Z     will be visible to the whole process.
2025-03-04T20:59:08.8373152Z     
2025-03-04T20:59:08.8373439Z     A planner subclass can expect the following sequence of calls during load_state_dict:
2025-03-04T20:59:08.8373542Z     
2025-03-04T20:59:08.8373901Z     1) set_up_planner - called on all ranks.
2025-03-04T20:59:08.8374060Z         Signals the start of loading a checkpoint.
2025-03-04T20:59:08.8374190Z     
2025-03-04T20:59:08.8374418Z     2) create_local_plan - called on all ranks.
2025-03-04T20:59:08.8374719Z         Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2025-03-04T20:59:08.8374824Z     
2025-03-04T20:59:08.8375018Z     3) create_global_plan - called on the coordinator rank only.
2025-03-04T20:59:08.8375241Z         Takes the LoadPlan from all ranks and make any global decision.
2025-03-04T20:59:08.8375333Z     
2025-03-04T20:59:08.8375486Z     4) load_bytes - called multiple times on each rank
2025-03-04T20:59:08.8375693Z         This is called once per non-tensor value in state_dict.
2025-03-04T20:59:08.8375782Z     
2025-03-04T20:59:08.8376025Z     5) resolve_tensor and commit_tensor - called multiple times on each rank
2025-03-04T20:59:08.8376219Z         They are called in pair for each Tensor value in state_dict.
2025-03-04T20:59:08.8376313Z     
2025-03-04T20:59:08.8376624Z     Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2025-03-04T20:59:08.8376834Z     most changes can be expressed by changes in a single method.
2025-03-04T20:59:08.8376921Z     
2025-03-04T20:59:08.8377070Z     There are two usual patterns of extension:
2025-03-04T20:59:08.8377205Z     
2025-03-04T20:59:08.8377478Z     Rewriting state_dict. This is the simplest way to extend the load process as it
2025-03-04T20:59:08.8377820Z     doesn't requite understanding the intrincacies of how LoadPlan works. We need
2025-03-04T20:59:08.8378073Z     to keep a reference to the original state_dict as load happens in place so
2025-03-04T20:59:08.8378208Z     we need to be able to perform it in place
2025-03-04T20:59:08.8378308Z     
2025-03-04T20:59:08.8378432Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8378589Z     >>> class RenamePlanner(DefaultLoadPlanner):
2025-03-04T20:59:08.8378697Z     >>>     def set_up_planner(
2025-03-04T20:59:08.8378805Z     >>>         self,
2025-03-04T20:59:08.8378926Z     >>>         state_dict: STATE_DICT_TYPE,
2025-03-04T20:59:08.8379049Z     >>>         metadata: Metadata,
2025-03-04T20:59:08.8379165Z     >>>         is_coordinator: bool,
2025-03-04T20:59:08.8379273Z     >>>     ) -> None:
2025-03-04T20:59:08.8379406Z     >>>         self.original_state_dict = state_dict
2025-03-04T20:59:08.8379603Z     >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2025-03-04T20:59:08.8379740Z     >>>
2025-03-04T20:59:08.8379882Z     >>>         if self.flatten_sharded_tensors:
2025-03-04T20:59:08.8380046Z     >>>             state_dict = _flatten_sharded_tensors(state_dict)
2025-03-04T20:59:08.8380143Z     >>>
2025-03-04T20:59:08.8380314Z     >>>         if self.flatten_state_dict:
2025-03-04T20:59:08.8380521Z     >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2025-03-04T20:59:08.8380612Z     >>>
2025-03-04T20:59:08.8380742Z     >>>         self.state_dict = state_dict
2025-03-04T20:59:08.8380858Z     >>>         self.metadata = metadata
2025-03-04T20:59:08.8381006Z     >>>         self.is_coordinator = is_coordinator
2025-03-04T20:59:08.8381096Z     >>>
2025-03-04T20:59:08.8381238Z     >>>     def load_bytes(self, read_item, value):
2025-03-04T20:59:08.8381350Z     >>> # Remove the "foo_" prefix
2025-03-04T20:59:08.8381694Z     >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2025-03-04T20:59:08.8381784Z     
2025-03-04T20:59:08.8381871Z     
2025-03-04T20:59:08.8382150Z     Modifying resolve_tensor and commit_tensor to handle load time transformation.
2025-03-04T20:59:08.8382237Z     
2025-03-04T20:59:08.8382374Z     >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8382538Z     >>> class MetaModelMaterialize(DefaultSavePlanner):
2025-03-04T20:59:08.8382676Z     >>>     def resolve_tensor(self, read_item):
2025-03-04T20:59:08.8382820Z     >>>         tensor = super().resolve_tensor(read_item)
2025-03-04T20:59:08.8382988Z     >>>         return torch.empty_like(tensor, device="cpu")
2025-03-04T20:59:08.8383078Z     >>>
2025-03-04T20:59:08.8383265Z     >>>     def commit_tensor(self, read_item, tensor):
2025-03-04T20:59:08.8383478Z     >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2025-03-04T20:59:08.8383579Z     
2025-03-04T20:59:08.8383841Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8383940Z     
2025-03-04T20:59:08.8384048Z       warnings.warn(msg)
2025-03-04T20:59:08.8384149Z     
2025-03-04T20:59:08.8384378Z --- Parse Warning: 57 / 116 ---
2025-03-04T20:59:08.8385382Z     /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=1106.
2025-03-04T20:59:08.8385658Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8385760Z     
2025-03-04T20:59:08.8385939Z     Return the model state_dict and optimizers state_dict.
2025-03-04T20:59:08.8386039Z     
2025-03-04T20:59:08.8386271Z     ``get_state_dict`` can process any module that is parallelized by PyTorch
2025-03-04T20:59:08.8386547Z     FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
2025-03-04T20:59:08.8386835Z     combination of these parallelisms. The main functions of ``get_state_dict``
2025-03-04T20:59:08.8387072Z     are: 1.) returning a model and optimizer state_dict that can be resharded
2025-03-04T20:59:08.8387289Z     with a different number of trainers and/or different parallelisms.
2025-03-04T20:59:08.8387558Z     2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
2025-03-04T20:59:08.8387656Z     these APIs.
2025-03-04T20:59:08.8387800Z     3.) sanity checking the result state_dict.
2025-03-04T20:59:08.8387887Z     
2025-03-04T20:59:08.8388117Z     The keys of the result state dictionary are the canonical FQNs (Fully
2025-03-04T20:59:08.8388365Z     Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
2025-03-04T20:59:08.8388623Z     position in an nn.Module hierarchy. More specifically, a canonical FQN to a
2025-03-04T20:59:08.8388837Z     parameter is the FQN returned by ``module.named_parameters()`` or
2025-03-04T20:59:08.8389062Z     ``module.named_buffers()`` when the module is not distributed by any
2025-03-04T20:59:08.8389354Z     parallelisms. Since the optimizer internally uses parameter IDs to represent
2025-03-04T20:59:08.8389590Z     a parameter, there will be a conversion from the parameter IDs to the
2025-03-04T20:59:08.8389741Z     canonical FQNs when calling this API.
2025-03-04T20:59:08.8389844Z     
2025-03-04T20:59:08.8390071Z     ``get_state_dict`` can also process a module that is not parallelized. In
2025-03-04T20:59:08.8390316Z     such a case, ``get_state_dict`` only performs one function -- converting the
2025-03-04T20:59:08.8390466Z     optimizer parameter IDs to the canonical FQNs.
2025-03-04T20:59:08.8390566Z     
2025-03-04T20:59:08.8390660Z     Example:
2025-03-04T20:59:08.8390780Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8390881Z         >>> import torch
2025-03-04T20:59:08.8391139Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:59:08.8391350Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-04T20:59:08.8391595Z         >>> from torch.distributed.checkpoint.state_dict import get_state_dict
2025-03-04T20:59:08.8391684Z     
2025-03-04T20:59:08.8391835Z         >>> fsdp_model = FSDP(copy.deepcopy(model))
2025-03-04T20:59:08.8392029Z         >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-04T20:59:08.8392170Z         >>> ddp_model = DDP(copy.deepcopy(model))
2025-03-04T20:59:08.8392355Z         >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-03-04T20:59:08.8392453Z     
2025-03-04T20:59:08.8392540Z     
2025-03-04T20:59:08.8392794Z         >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
2025-03-04T20:59:08.8392997Z         >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
2025-03-04T20:59:08.8393126Z         ...     fsdp_model, fsdp_optim
2025-03-04T20:59:08.8393218Z         ... )
2025-03-04T20:59:08.8393302Z     
2025-03-04T20:59:08.8393535Z         >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
2025-03-04T20:59:08.8393645Z         >>> # the asserts will fail.
2025-03-04T20:59:08.8393793Z         >>> assert ddp_state_dict == fsdp_state_dict
2025-03-04T20:59:08.8393951Z         >>> assert ddp_optim_state == fsdp_optim_state_dict
2025-03-04T20:59:08.8394050Z     
2025-03-04T20:59:08.8394140Z     
2025-03-04T20:59:08.8394241Z     Args:
2025-03-04T20:59:08.8394391Z         model (nn.Module): the nn.Module to the model.
2025-03-04T20:59:08.8394597Z         optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
2025-03-04T20:59:08.8394763Z             The optimizers that are used to optimize ``model``.
2025-03-04T20:59:08.8395066Z         submodules (deprecated): Optional[Set[nn.Module]]: only return the model parameters
2025-03-04T20:59:08.8395186Z             that belong to the submodules.
2025-03-04T20:59:08.8395380Z         options (StateDictOptions): the options to control how
2025-03-04T20:59:08.8395623Z             model state_dict and optimizer state_dict should be returned. See
2025-03-04T20:59:08.8395766Z             `StateDictOptions` for the details.
2025-03-04T20:59:08.8395857Z     
2025-03-04T20:59:08.8395962Z     Returns:
2025-03-04T20:59:08.8396164Z         ``Tuple`` that contain model state_dict and optimizer state_dict.
2025-03-04T20:59:08.8396264Z     
2025-03-04T20:59:08.8396499Z     :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
2025-03-04T20:59:08.8396596Z     
2025-03-04T20:59:08.8396855Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8396953Z     
2025-03-04T20:59:08.8397058Z       warnings.warn(msg)
2025-03-04T20:59:08.8397156Z     
2025-03-04T20:59:08.8397360Z --- Parse Warning: 58 / 116 ---
2025-03-04T20:59:08.8398344Z     /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=62.
2025-03-04T20:59:08.8398618Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8398747Z     
2025-03-04T20:59:08.8398950Z     Load a checkpoint into a distributed state dict in SPMD style.
2025-03-04T20:59:08.8399049Z     
2025-03-04T20:59:08.8399304Z     Each rank must have the same keys in their ``state_dict`` provided to this
2025-03-04T20:59:08.8399558Z     API. Mismatched keys may result in hangs or errors. If unsure, you can use
2025-03-04T20:59:08.8399785Z     the ``utils._assert_same_keys`` API to check (but may incur communication
2025-03-04T20:59:08.8399890Z     costs).
2025-03-04T20:59:08.8399981Z     
2025-03-04T20:59:08.8400185Z     Each rank will try to read the least amount of data necessary
2025-03-04T20:59:08.8400436Z     to fullfill the requested `state_dict`. When loading :class:`ShardedTensor`
2025-03-04T20:59:08.8400706Z     or :class:`DTensor` instances, each rank only reads data for their local shards.
2025-03-04T20:59:08.8400797Z     
2025-03-04T20:59:08.8401080Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-04T20:59:08.8401348Z     load will first call ``state_dict`` before attempting deserialization, followed by
2025-03-04T20:59:08.8401545Z     ``load_state_dict`` once the deserialization is complete.
2025-03-04T20:59:08.8401813Z     For each non-``Stateful`` object, load will deserailize the object, and then replace
2025-03-04T20:59:08.8401987Z     it in the ``state_dict`` with the deserialized object.
2025-03-04T20:59:08.8402076Z     
2025-03-04T20:59:08.8402202Z     .. warning::
2025-03-04T20:59:08.8402378Z         All tensors in ``state_dict`` must be allocated on their
2025-03-04T20:59:08.8402593Z         destination device *prior to* calling this function.
2025-03-04T20:59:08.8402683Z     
2025-03-04T20:59:08.8402933Z         All non-tensor data is loaded using `torch.load()` and modified in place
2025-03-04T20:59:08.8403036Z         on state_dict.
2025-03-04T20:59:08.8403125Z     
2025-03-04T20:59:08.8403238Z     .. warning::
2025-03-04T20:59:08.8403455Z         Users must call `load_state_dict` on the root module to ensure load
2025-03-04T20:59:08.8403663Z         pos-processing and non-tensor data properly propagates.
2025-03-04T20:59:08.8403750Z     
2025-03-04T20:59:08.8403859Z     .. note:
2025-03-04T20:59:08.8404095Z         If no process group is initialized, this function will assume the intent
2025-03-04T20:59:08.8404343Z         is to load a checkpoint into the local process. This can be useful in the
2025-03-04T20:59:08.8404598Z         case of local inference, and when using regular Tensors (as opposed to DTensor
2025-03-04T20:59:08.8404726Z          or ShardedTensor)
2025-03-04T20:59:08.8404816Z     
2025-03-04T20:59:08.8404926Z     .. note:
2025-03-04T20:59:08.8405075Z         Rank 0 is assumed to be the coordinator rank.
2025-03-04T20:59:08.8405180Z     
2025-03-04T20:59:08.8405300Z     Args:
2025-03-04T20:59:08.8405537Z         state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
2025-03-04T20:59:08.8405695Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-03-04T20:59:08.8405927Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-04T20:59:08.8406139Z             depends on the storage. It can be a path to a folder or to a file.
2025-03-04T20:59:08.8406323Z             It can also be a key if the storage is a key-value store.
2025-03-04T20:59:08.8406427Z             (Default: ``None``)
2025-03-04T20:59:08.8406579Z         storage_reader (Optional[StorageReader]):
2025-03-04T20:59:08.8406795Z             Instance of StorageWriter used to perform reads. If this is not
2025-03-04T20:59:08.8407023Z             specified, DCP will automatically infer the reader based on the
2025-03-04T20:59:08.8407232Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-04T20:59:08.8407361Z             be raised. (Default: ``None``)
2025-03-04T20:59:08.8407487Z         planner (Optional[LoadPlanner]):
2025-03-04T20:59:08.8407708Z             Instance of LoadPlanner. If this is not specificed, the default
2025-03-04T20:59:08.8407875Z             planner will be used. (Default: ``None``)
2025-03-04T20:59:08.8408022Z         process_group (Optional[ProcessGroup]):
2025-03-04T20:59:08.8408236Z             ProcessGroup to be used for cross-rank synchronization.
2025-03-04T20:59:08.8408354Z             (Default: ``None``)
2025-03-04T20:59:08.8408574Z         no_dist (bool): If ``True``, this function will assume the intent is to load
2025-03-04T20:59:08.8408849Z             a checkpoint without using cross-rank synchronization. (Default: ``False``)
2025-03-04T20:59:08.8408945Z     Returns:
2025-03-04T20:59:08.8409050Z         None.
2025-03-04T20:59:08.8409139Z     
2025-03-04T20:59:08.8409246Z     Examples
2025-03-04T20:59:08.8409355Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8409476Z         >>> my_model = MyModule()
2025-03-04T20:59:08.8409627Z         >>> optimizer = Adagrad(my_model.parameters())
2025-03-04T20:59:08.8409776Z         >>> model_state_dict = my_model.state_dict()
2025-03-04T20:59:08.8410022Z         >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
2025-03-04T20:59:08.8410141Z         ...     "/checkpoint/1"
2025-03-04T20:59:08.8410231Z         ... )
2025-03-04T20:59:08.8410333Z     
2025-03-04T20:59:08.8410501Z         >>> torch.distributed.checkpoint.load_state_dict(
2025-03-04T20:59:08.8410622Z         >>>     state_dict=model_state_dict,
2025-03-04T20:59:08.8410762Z         >>>     storage_reader=fs_storage_reader,
2025-03-04T20:59:08.8410852Z         >>> )
2025-03-04T20:59:08.8410950Z     
2025-03-04T20:59:08.8411181Z         >>> # module.load_state_dict() function might have customized steps
2025-03-04T20:59:08.8411332Z         >>> # to flush the state_dict, must call it to
2025-03-04T20:59:08.8411446Z         >>> # ensure correct behavior.
2025-03-04T20:59:08.8411597Z         >>> my_model.load_state_dict(model_state_dict)
2025-03-04T20:59:08.8411683Z     
2025-03-04T20:59:08.8411788Z     .. note::
2025-03-04T20:59:08.8412008Z         load_state_dict uses collectives to coordinate reads across ranks.
2025-03-04T20:59:08.8412239Z         For NCCL-based process groups, internal tensor representations of
2025-03-04T20:59:08.8412477Z         objects must be moved to the GPU device before communication takes place.
2025-03-04T20:59:08.8412718Z         In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-04T20:59:08.8412956Z         and it is the user's responsibility to ensure that this is set so that each
2025-03-04T20:59:08.8413160Z         rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-04T20:59:08.8413268Z     
2025-03-04T20:59:08.8413539Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8413628Z     
2025-03-04T20:59:08.8413748Z       warnings.warn(msg)
2025-03-04T20:59:08.8413862Z     
2025-03-04T20:59:08.8414087Z --- Parse Warning: 59 / 116 ---
2025-03-04T20:59:08.8415058Z     /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=68.
2025-03-04T20:59:08.8415352Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8415443Z     
2025-03-04T20:59:08.8415589Z     Save a distributed model in SPMD style.
2025-03-04T20:59:08.8415680Z     
2025-03-04T20:59:08.8415898Z     This function is different from ``torch.save()`` as it handles
2025-03-04T20:59:08.8416168Z     ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2025-03-04T20:59:08.8416268Z     
2025-03-04T20:59:08.8416533Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-03-04T20:59:08.8416704Z     save will call ``state_dict`` before serialization.
2025-03-04T20:59:08.8416795Z     
2025-03-04T20:59:08.8416905Z     .. warning::
2025-03-04T20:59:08.8417200Z         There is no guarantees of Backwards Compatibility across PyTorch versions
2025-03-04T20:59:08.8417321Z         for saved state_dicts.
2025-03-04T20:59:08.8417407Z     
2025-03-04T20:59:08.8417514Z     .. warning::
2025-03-04T20:59:08.8417842Z         If using the `process_group` argument, make sure that only its ranks
2025-03-04T20:59:08.8418080Z         call `save_state_dict` and that all data in state_dict belong to it.
2025-03-04T20:59:08.8418168Z     
2025-03-04T20:59:08.8418277Z     .. note::
2025-03-04T20:59:08.8418543Z         When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2025-03-04T20:59:08.8418825Z         the shard_group should be calling `save_state_dict` and the corresponding process
2025-03-04T20:59:08.8418942Z         group needs to be passed in.
2025-03-04T20:59:08.8419043Z     
2025-03-04T20:59:08.8419138Z     .. note::
2025-03-04T20:59:08.8419428Z         If no process group is available, this function assumes the intention is to save the
2025-03-04T20:59:08.8419592Z          state_dict in the local process.
2025-03-04T20:59:08.8419692Z     
2025-03-04T20:59:08.8419783Z     .. note:
2025-03-04T20:59:08.8419932Z         Rank 0 is assumed to be the coordinator rank.
2025-03-04T20:59:08.8420031Z     
2025-03-04T20:59:08.8420119Z     
2025-03-04T20:59:08.8420221Z     Args:
2025-03-04T20:59:08.8420384Z         state_dict (Dict[str, Any]): The state_dict to save.
2025-03-04T20:59:08.8420550Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-03-04T20:59:08.8420768Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-04T20:59:08.8421020Z             depends on the storage. It can be a path to a folder or to a file.
2025-03-04T20:59:08.8421196Z             It can also be a key if the storage is a key-value store.
2025-03-04T20:59:08.8421314Z             (Default: ``None``)
2025-03-04T20:59:08.8421454Z         storage_writer (Optional[StorageWriter]):
2025-03-04T20:59:08.8421685Z             Instance of StorageWriter used to perform writes. If this is not
2025-03-04T20:59:08.8421900Z             specified, DCP will automatically infer the writer based on the
2025-03-04T20:59:08.8422122Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-04T20:59:08.8422240Z             be raised. (Default: ``None``)
2025-03-04T20:59:08.8422378Z         planner (Optional[SavePlanner]):
2025-03-04T20:59:08.8422588Z             Instance of SavePlanner. If this is not specificed, the default
2025-03-04T20:59:08.8422738Z             planner will be used. (Default: ``None``)
2025-03-04T20:59:08.8422881Z         process_group (Optional[ProcessGroup]):
2025-03-04T20:59:08.8423082Z             ProcessGroup to be used for cross-rank synchronization.
2025-03-04T20:59:08.8423186Z             (Default: ``None``)
2025-03-04T20:59:08.8423300Z         no_dist (bool):
2025-03-04T20:59:08.8423507Z             If ``True``, this function will assume the intent is to load
2025-03-04T20:59:08.8423700Z             a checkpoint without using cross-rank synchronization.
2025-03-04T20:59:08.8423809Z             (Default: ``False``)
2025-03-04T20:59:08.8423908Z     
2025-03-04T20:59:08.8424001Z     Returns:
2025-03-04T20:59:08.8424181Z         Metadata: Metadata object for the saved checkpoint.
2025-03-04T20:59:08.8424269Z     
2025-03-04T20:59:08.8424376Z     Example:
2025-03-04T20:59:08.8424483Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8424602Z         >>> my_model = MyModule()
2025-03-04T20:59:08.8424687Z     
2025-03-04T20:59:08.8424818Z         >>> state_dict = {"model": my_model}
2025-03-04T20:59:08.8424910Z     
2025-03-04T20:59:08.8425169Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-04T20:59:08.8425274Z         ...     "/checkpoint/1"
2025-03-04T20:59:08.8425376Z         ... )
2025-03-04T20:59:08.8425515Z         >>> torch.distributed.checkpoint.save(
2025-03-04T20:59:08.8425630Z         >>>     state_dict=state_dict,
2025-03-04T20:59:08.8425770Z         >>>     storage_writer=fs_storage_writer,
2025-03-04T20:59:08.8425888Z         >>> )
2025-03-04T20:59:08.8425985Z     
2025-03-04T20:59:08.8426077Z     .. note::
2025-03-04T20:59:08.8426335Z         save_state_dict uses collectives to coordinate writes across ranks.
2025-03-04T20:59:08.8426558Z         For NCCL-based process groups, internal tensor representations of
2025-03-04T20:59:08.8426809Z         objects must be moved to the GPU device before communication takes place.
2025-03-04T20:59:08.8427034Z         In this case, the device used is given by ``torch.cuda.current_device()``
2025-03-04T20:59:08.8427264Z         and it is the user's responsibility to ensure that this is set so that
2025-03-04T20:59:08.8427469Z         each rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-03-04T20:59:08.8427568Z     
2025-03-04T20:59:08.8427827Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8427930Z     
2025-03-04T20:59:08.8428035Z       warnings.warn(msg)
2025-03-04T20:59:08.8428139Z     
2025-03-04T20:59:08.8428352Z --- Parse Warning: 60 / 116 ---
2025-03-04T20:59:08.8429380Z     /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=177.
2025-03-04T20:59:08.8429656Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8429943Z     Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2025-03-04T20:59:08.8430274Z         staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2025-03-04T20:59:08.8430374Z     
2025-03-04T20:59:08.8430475Z         .. warning::
2025-03-04T20:59:08.8430656Z             This feature is experimental and subject to change.
2025-03-04T20:59:08.8430748Z     
2025-03-04T20:59:08.8430852Z         Args:
2025-03-04T20:59:08.8431021Z             state_dict (Dict[str, Any]): The state_dict to save.
2025-03-04T20:59:08.8431193Z             checkpoint_id (Union[str, os.PathLike, None]):
2025-03-04T20:59:08.8431418Z                 The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-03-04T20:59:08.8431644Z                 depends on the storage. It can be a path to a folder or to a file.
2025-03-04T20:59:08.8431819Z                 It can also be a key if the storage is a key-value store.
2025-03-04T20:59:08.8431935Z                 (Default: ``None``)
2025-03-04T20:59:08.8432080Z             storage_writer (Optional[StorageWriter]):
2025-03-04T20:59:08.8432311Z                 Instance of StorageWriter used to perform 'stage' and  'save'. If
2025-03-04T20:59:08.8432556Z                 this is not specified, DCP will automatically infer the writer based on the
2025-03-04T20:59:08.8432804Z                 checkpoint_id. If checkpoint_id is also None, an exception will
2025-03-04T20:59:08.8432927Z                 be raised. (Default: ``None``)
2025-03-04T20:59:08.8433069Z             planner (Optional[SavePlanner]):
2025-03-04T20:59:08.8433282Z                 Instance of SavePlanner. If this is not specificed, the default
2025-03-04T20:59:08.8433438Z                 planner will be used. (Default: ``None``)
2025-03-04T20:59:08.8433580Z             process_group (Optional[ProcessGroup]):
2025-03-04T20:59:08.8433782Z                 ProcessGroup to be used for cross-rank synchronization.
2025-03-04T20:59:08.8433889Z                 (Default: ``None``)
2025-03-04T20:59:08.8433996Z     
2025-03-04T20:59:08.8434092Z         Returns:
2025-03-04T20:59:08.8434327Z             Future: A future holding the resultant Metadata object from `save`.
2025-03-04T20:59:08.8434418Z     
2025-03-04T20:59:08.8434525Z         Example:
2025-03-04T20:59:08.8434631Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8434756Z             >>> my_model = MyModule()
2025-03-04T20:59:08.8434846Z     
2025-03-04T20:59:08.8434982Z             >>> state_dict = {"model": my_model}
2025-03-04T20:59:08.8435098Z     
2025-03-04T20:59:08.8435352Z             >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-03-04T20:59:08.8435460Z             ...     "/checkpoint/1"
2025-03-04T20:59:08.8435586Z             ... )
2025-03-04T20:59:08.8435804Z             >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2025-03-04T20:59:08.8435930Z             >>>     state_dict=state_dict,
2025-03-04T20:59:08.8436061Z             >>>     storage_writer=fs_storage_writer,
2025-03-04T20:59:08.8436162Z             >>> )
2025-03-04T20:59:08.8436256Z             >>>
2025-03-04T20:59:08.8436364Z             >>> # ... do some work ...
2025-03-04T20:59:08.8436462Z             >>>
2025-03-04T20:59:08.8436580Z             >>> checkpoint_future.result()
2025-03-04T20:59:08.8436678Z     
2025-03-04T20:59:08.8436768Z         
2025-03-04T20:59:08.8437042Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8437133Z     
2025-03-04T20:59:08.8437248Z       warnings.warn(msg)
2025-03-04T20:59:08.8437336Z     
2025-03-04T20:59:08.8437545Z --- Parse Warning: 61 / 116 ---
2025-03-04T20:59:08.8438700Z     /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.
2025-03-04T20:59:08.8438985Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8439073Z     
2025-03-04T20:59:08.8439328Z     Initialize rendezvous event object and record its operations.
2025-03-04T20:59:08.8439418Z     
2025-03-04T20:59:08.8439518Z     Args:
2025-03-04T20:59:08.8439659Z         run_id (str): The run id of the rendezvous.
2025-03-04T20:59:08.8439830Z         message (str): The message describing the event.
2025-03-04T20:59:08.8440093Z         node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2025-03-04T20:59:08.8440295Z         name (str): Event name. (E.g. Current action being performed).
2025-03-04T20:59:08.8440422Z         hostname (str): Hostname of the node.
2025-03-04T20:59:08.8440587Z         pid (Optional[int]): The process id of the node.
2025-03-04T20:59:08.8440840Z         master_endpoint (str): The master endpoint for the rendezvous store, if known.
2025-03-04T20:59:08.8441126Z         local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2025-03-04T20:59:08.8441293Z         rank (Optional[int]): The rank of the node, if known.
2025-03-04T20:59:08.8441395Z     Returns:
2025-03-04T20:59:08.8441486Z         None
2025-03-04T20:59:08.8441590Z     Example:
2025-03-04T20:59:08.8441727Z         >>> # See DynamicRendezvousHandler class
2025-03-04T20:59:08.8441860Z         >>> def _record(
2025-03-04T20:59:08.8441954Z         ...     self,
2025-03-04T20:59:08.8442067Z         ...     message: str,
2025-03-04T20:59:08.8442217Z         ...     node_state: NodeState = NodeState.RUNNING,
2025-03-04T20:59:08.8442346Z         ...     rank: Optional[int] = None,
2025-03-04T20:59:08.8442441Z         ... ) -> None:
2025-03-04T20:59:08.8442580Z         ...     construct_and_record_rdzv_event(
2025-03-04T20:59:08.8442755Z         ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2025-03-04T20:59:08.8442889Z         ...         run_id=self._settings.run_id,
2025-03-04T20:59:08.8442993Z         ...         message=message,
2025-03-04T20:59:08.8443115Z         ...         node_state=node_state,
2025-03-04T20:59:08.8443244Z         ...         hostname=self._this_node.addr,
2025-03-04T20:59:08.8443371Z         ...         pid=self._this_node.pid,
2025-03-04T20:59:08.8443504Z         ...         local_id=self._this_node.local_id,
2025-03-04T20:59:08.8443611Z         ...         rank=rank,
2025-03-04T20:59:08.8443705Z         ...     )
2025-03-04T20:59:08.8443790Z     
2025-03-04T20:59:08.8444065Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8444182Z     
2025-03-04T20:59:08.8444300Z       warnings.warn(msg)
2025-03-04T20:59:08.8444386Z     
2025-03-04T20:59:08.8444629Z --- Parse Warning: 62 / 116 ---
2025-03-04T20:59:08.8445554Z     /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.
2025-03-04T20:59:08.8445835Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8445924Z     
2025-03-04T20:59:08.8446110Z     This configures FSDP-native mixed precision training.
2025-03-04T20:59:08.8446196Z     
2025-03-04T20:59:08.8446306Z     Attributes:
2025-03-04T20:59:08.8446550Z         param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2025-03-04T20:59:08.8446778Z             parameters during forward and backward and thus the dtype for
2025-03-04T20:59:08.8447012Z             forward and backward computation. Outside forward and backward, the
2025-03-04T20:59:08.8447262Z             *sharded* parameters are kept in full precision (e.g. for the
2025-03-04T20:59:08.8447478Z             optimizer step), and for model checkpointing, the parameters are
2025-03-04T20:59:08.8447655Z             always saved in full precision. (Default: ``None``)
2025-03-04T20:59:08.8447874Z         reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-04T20:59:08.8448107Z             gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2025-03-04T20:59:08.8448314Z             ``None`` but ``param_dtype`` is not ``None``, then this takes on
2025-03-04T20:59:08.8448533Z             the ``param_dtype`` value, still running gradient reduction in low
2025-03-04T20:59:08.8448753Z             precision. This is permitted to differ from ``param_dtype``, e.g.
2025-03-04T20:59:08.8448968Z             to force gradient reduction to run in full precision. (Default:
2025-03-04T20:59:08.8449063Z             ``None``)
2025-03-04T20:59:08.8449291Z         buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-03-04T20:59:08.8449498Z             buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2025-03-04T20:59:08.8449713Z             ``buffer_dtype`` in the first forward pass and keeps them in that
2025-03-04T20:59:08.8449930Z             dtype thereafter. For model checkpointing, the buffers are saved
2025-03-04T20:59:08.8450129Z             in full precision except for ``LOCAL_STATE_DICT``. (Default:
2025-03-04T20:59:08.8450225Z             ``None``)
2025-03-04T20:59:08.8450439Z         keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2025-03-04T20:59:08.8450662Z             gradients to full precision after the backward pass in preparation
2025-03-04T20:59:08.8450921Z             for the optimizer step. If ``True``, then FSDP keeps the gradients
2025-03-04T20:59:08.8451131Z             in the dtype used for gradient reduction, which can save memory if
2025-03-04T20:59:08.8451354Z             using a custom optimizer that supports running in low precision.
2025-03-04T20:59:08.8451461Z             (Default: ``False``)
2025-03-04T20:59:08.8451690Z         cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2025-03-04T20:59:08.8451899Z             its forward args and kwargs to ``param_dtype``. This is to ensure
2025-03-04T20:59:08.8452128Z             that parameter and input dtypes match for forward computation, as
2025-03-04T20:59:08.8452343Z             required by many ops. This may need to be set to ``True`` when only
2025-03-04T20:59:08.8452582Z             applying mixed precision to some but not all FSDP modules, in which
2025-03-04T20:59:08.8452796Z             case a mixed-precision FSDP submodule needs to recast its inputs.
2025-03-04T20:59:08.8452918Z             (Default: ``False``)
2025-03-04T20:59:08.8453145Z         cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2025-03-04T20:59:08.8453388Z             casts its forward args and kwargs to ``param_dtype``, overriding
2025-03-04T20:59:08.8453607Z             the value of ``cast_forward_inputs``. For non-root FSDP modules,
2025-03-04T20:59:08.8453766Z             this does not do anything. (Default: ``True``)
2025-03-04T20:59:08.8453992Z         _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2025-03-04T20:59:08.8454188Z             module classes to ignore for mixed precision when using an
2025-03-04T20:59:08.8454377Z             ``auto_wrap_policy``: Modules of these classes will have FSDP
2025-03-04T20:59:08.8454602Z             applied to them separately with mixed precision disabled (meaning
2025-03-04T20:59:08.8454817Z             that the final FSDP construction would deviate from the specified
2025-03-04T20:59:08.8455027Z             policy). If ``auto_wrap_policy`` is not specified, then this does
2025-03-04T20:59:08.8455236Z             not do anything. This API is experimental and subject to change.
2025-03-04T20:59:08.8455363Z             (Default: ``(_BatchNorm,)``)
2025-03-04T20:59:08.8455452Z     
2025-03-04T20:59:08.8455645Z     .. note:: This API is experimental and subject to change.
2025-03-04T20:59:08.8455733Z     
2025-03-04T20:59:08.8455974Z     .. note:: Only floating point tensors are cast to their specified dtypes.
2025-03-04T20:59:08.8456063Z     
2025-03-04T20:59:08.8456263Z     .. note:: In ``summon_full_params``, parameters are forced to full
2025-03-04T20:59:08.8456387Z         precision, but buffers are not.
2025-03-04T20:59:08.8456484Z     
2025-03-04T20:59:08.8456721Z     .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2025-03-04T20:59:08.8456945Z         their inputs are in a low precision like ``float16`` or ``bfloat16``.
2025-03-04T20:59:08.8457183Z         Disabling FSDP's mixed precision for those norm modules only means that
2025-03-04T20:59:08.8457415Z         the affine parameters are kept in ``float32``. However, this incurs
2025-03-04T20:59:08.8457660Z         separate all-gathers and reduce-scatters for those norm modules, which
2025-03-04T20:59:08.8457972Z         may be inefficient, so if the workload permits, the user should prefer
2025-03-04T20:59:08.8458134Z         to still apply mixed precision to those modules.
2025-03-04T20:59:08.8458232Z     
2025-03-04T20:59:08.8458445Z     .. note:: By default, if the user passes a model with any ``_BatchNorm``
2025-03-04T20:59:08.8458667Z         modules and specifies an ``auto_wrap_policy``, then the batch norm
2025-03-04T20:59:08.8458901Z         modules will have FSDP applied to them separately with mixed precision
2025-03-04T20:59:08.8459094Z         disabled. See the ``_module_classes_to_ignore`` argument.
2025-03-04T20:59:08.8459184Z     
2025-03-04T20:59:08.8459410Z     .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2025-03-04T20:59:08.8459665Z         ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2025-03-04T20:59:08.8459858Z         its ``cast_root_forward_inputs`` takes precedence over its
2025-03-04T20:59:08.8460044Z         ``cast_forward_inputs``. For non-root FSDP instances, their
2025-03-04T20:59:08.8460280Z         ``cast_root_forward_inputs`` values are ignored. The default setting is
2025-03-04T20:59:08.8460513Z         sufficient for the typical case where each FSDP instance has the same
2025-03-04T20:59:08.8460758Z         ``MixedPrecision`` configuration and only needs to cast inputs to the
2025-03-04T20:59:08.8460951Z         ``param_dtype`` at the beginning of the model's forward pass.
2025-03-04T20:59:08.8461051Z     
2025-03-04T20:59:08.8461265Z     .. note:: For nested FSDP instances with different ``MixedPrecision``
2025-03-04T20:59:08.8461522Z         configurations, we recommend setting individual ``cast_forward_inputs``
2025-03-04T20:59:08.8461736Z         values to configure casting inputs or not before each instance's
2025-03-04T20:59:08.8461948Z         forward. In such a case, since the casts happen before each FSDP
2025-03-04T20:59:08.8462203Z         instance's forward, a parent FSDP instance should have its non-FSDP
2025-03-04T20:59:08.8462479Z         submodules run before its FSDP submodules to avoid the activation dtype
2025-03-04T20:59:08.8462697Z         being changed due to a different ``MixedPrecision`` configuration.
2025-03-04T20:59:08.8462796Z     
2025-03-04T20:59:08.8462893Z         Example::
2025-03-04T20:59:08.8462994Z     
2025-03-04T20:59:08.8463135Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8463325Z             >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2025-03-04T20:59:08.8463432Z             >>> model[1] = FSDP(
2025-03-04T20:59:08.8463547Z             >>>     model[1],
2025-03-04T20:59:08.8463864Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2025-03-04T20:59:08.8463972Z             >>> )
2025-03-04T20:59:08.8464076Z             >>> model = FSDP(
2025-03-04T20:59:08.8464190Z             >>>     model,
2025-03-04T20:59:08.8464507Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2025-03-04T20:59:08.8464607Z             >>> )
2025-03-04T20:59:08.8464699Z     
2025-03-04T20:59:08.8464932Z         The above shows a working example. On the other hand, if ``model[1]``
2025-03-04T20:59:08.8465142Z         were replaced with ``model[0]``, meaning that the submodule using
2025-03-04T20:59:08.8465385Z         different ``MixedPrecision`` ran its forward first, then ``model[1]``
2025-03-04T20:59:08.8465639Z         would incorrectly see ``float16`` activations instead of ``bfloat16``
2025-03-04T20:59:08.8465743Z         ones.
2025-03-04T20:59:08.8465833Z     
2025-03-04T20:59:08.8465933Z     
2025-03-04T20:59:08.8466192Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8466294Z     
2025-03-04T20:59:08.8466402Z       warnings.warn(msg)
2025-03-04T20:59:08.8466501Z     
2025-03-04T20:59:08.8466712Z --- Parse Warning: 63 / 116 ---
2025-03-04T20:59:08.8467681Z     /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.
2025-03-04T20:59:08.8467953Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8468050Z     
2025-03-04T20:59:08.8468262Z     ``FullStateDictConfig`` is a config class meant to be used with
2025-03-04T20:59:08.8468481Z     ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
2025-03-04T20:59:08.8468691Z     ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
2025-03-04T20:59:08.8468930Z     dicts to save GPU memory and CPU memory, respectively. This config class
2025-03-04T20:59:08.8469163Z     is meant to be used via the :func:`state_dict_type` context manager as
2025-03-04T20:59:08.8469267Z     follows:
2025-03-04T20:59:08.8469353Z     
2025-03-04T20:59:08.8469500Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8469745Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:59:08.8469892Z         >>> fsdp = FSDP(model, auto_wrap_policy=...)
2025-03-04T20:59:08.8470104Z         >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
2025-03-04T20:59:08.8470339Z         >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
2025-03-04T20:59:08.8470461Z         >>>     state = fsdp.state_dict()
2025-03-04T20:59:08.8470686Z         >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
2025-03-04T20:59:08.8470933Z         >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
2025-03-04T20:59:08.8471184Z         >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
2025-03-04T20:59:08.8471296Z         >>> if dist.get_rank() == 0:
2025-03-04T20:59:08.8471518Z         >>> # Load checkpoint only on rank 0 to avoid memory redundancy
2025-03-04T20:59:08.8471668Z         >>>     state_dict = torch.load("my_checkpoint.pt")
2025-03-04T20:59:08.8471946Z         >>>     model.load_state_dict(state_dict)
2025-03-04T20:59:08.8472191Z         >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
2025-03-04T20:59:08.8472451Z         >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
2025-03-04T20:59:08.8472552Z         >>> fsdp = FSDP(
2025-03-04T20:59:08.8472656Z         ...     model,
2025-03-04T20:59:08.8472801Z         ...     device_id=torch.cuda.current_device(),
2025-03-04T20:59:08.8472923Z         ...     auto_wrap_policy=...,
2025-03-04T20:59:08.8473037Z         ...     sync_module_states=True,
2025-03-04T20:59:08.8473129Z         ... )
2025-03-04T20:59:08.8473361Z         >>> # After this point, all ranks have FSDP model with loaded checkpoint.
2025-03-04T20:59:08.8473451Z     
2025-03-04T20:59:08.8473560Z     Attributes:
2025-03-04T20:59:08.8473996Z         rank0_only (bool): If ``True``, then only rank 0 saves the full state
2025-03-04T20:59:08.8474223Z             dict, and nonzero ranks save an empty dict. If ``False``, then all
2025-03-04T20:59:08.8474390Z             ranks save the full state dict. (Default: ``False``)
2025-03-04T20:59:08.8474490Z     
2025-03-04T20:59:08.8474751Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8474848Z     
2025-03-04T20:59:08.8474954Z       warnings.warn(msg)
2025-03-04T20:59:08.8475053Z     
2025-03-04T20:59:08.8475343Z --- Parse Warning: 64 / 116 ---
2025-03-04T20:59:08.8476548Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=639.
2025-03-04T20:59:08.8476821Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8477092Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-04T20:59:08.8477181Z     
2025-03-04T20:59:08.8477463Z             Also takes (optional) configuration for the model's and optimizer's state dict.
2025-03-04T20:59:08.8477678Z             The target module does not have to be a FSDP module. If the target
2025-03-04T20:59:08.8477909Z             module is a FSDP module, its ``state_dict_type`` will also be changed.
2025-03-04T20:59:08.8477997Z     
2025-03-04T20:59:08.8478216Z             .. note:: This API should be called for only the top-level (root)
2025-03-04T20:59:08.8478312Z                 module.
2025-03-04T20:59:08.8478412Z     
2025-03-04T20:59:08.8478630Z             .. note:: This API enables users to transparently use the conventional
2025-03-04T20:59:08.8478881Z                 ``state_dict`` API to take model checkpoints in cases where the
2025-03-04T20:59:08.8479103Z                 root FSDP module is wrapped by another ``nn.Module``. For example,
2025-03-04T20:59:08.8479332Z                 the following will ensure ``state_dict`` is called on all non-FSDP
2025-03-04T20:59:08.8479576Z                 instances, while dispatching into `sharded_state_dict` implementation
2025-03-04T20:59:08.8479688Z                 for FSDP:
2025-03-04T20:59:08.8479777Z     
2025-03-04T20:59:08.8479883Z             Example::
2025-03-04T20:59:08.8479971Z     
2025-03-04T20:59:08.8480124Z                 >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8480244Z                 >>> model = DDP(FSDP(...))
2025-03-04T20:59:08.8480382Z                 >>> FSDP.set_state_dict_type(
2025-03-04T20:59:08.8480483Z                 >>>     model,
2025-03-04T20:59:08.8480637Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-04T20:59:08.8480865Z                 >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2025-03-04T20:59:08.8481148Z                 >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2025-03-04T20:59:08.8481242Z                 >>> )
2025-03-04T20:59:08.8481419Z                 >>> param_state_dict = model.state_dict()
2025-03-04T20:59:08.8481601Z                 >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-04T20:59:08.8481696Z     
2025-03-04T20:59:08.8481788Z             Args:
2025-03-04T20:59:08.8481934Z                 module (torch.nn.Module): Root module.
2025-03-04T20:59:08.8482179Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-04T20:59:08.8482438Z                 state_dict_config (Optional[StateDictConfig]): the configuration for the
2025-03-04T20:59:08.8482560Z                     target ``state_dict_type``.
2025-03-04T20:59:08.8482832Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2025-03-04T20:59:08.8482958Z                     for the optimizer state dict.
2025-03-04T20:59:08.8483057Z     
2025-03-04T20:59:08.8483149Z             Returns:
2025-03-04T20:59:08.8483390Z                 A StateDictSettings that include the previous state_dict type and
2025-03-04T20:59:08.8483519Z                 configuration for the module.
2025-03-04T20:59:08.8483619Z             
2025-03-04T20:59:08.8483878Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8483975Z     
2025-03-04T20:59:08.8484079Z       warnings.warn(msg)
2025-03-04T20:59:08.8484178Z     
2025-03-04T20:59:08.8484377Z --- Parse Warning: 65 / 116 ---
2025-03-04T20:59:08.8485604Z     /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=797.
2025-03-04T20:59:08.8485879Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8486146Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-03-04T20:59:08.8486235Z     
2025-03-04T20:59:08.8486572Z             This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2025-03-04T20:59:08.8486711Z             :meth:`set_state_dict_type` for the detail.
2025-03-04T20:59:08.8486808Z     
2025-03-04T20:59:08.8486906Z             Example::
2025-03-04T20:59:08.8487002Z     
2025-03-04T20:59:08.8487143Z                 >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8487270Z                 >>> model = DDP(FSDP(...))
2025-03-04T20:59:08.8487394Z                 >>> with FSDP.state_dict_type(
2025-03-04T20:59:08.8487509Z                 >>>     model,
2025-03-04T20:59:08.8487650Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2025-03-04T20:59:08.8487769Z                 >>> ):
2025-03-04T20:59:08.8487914Z                 >>>     checkpoint = model.state_dict()
2025-03-04T20:59:08.8488000Z     
2025-03-04T20:59:08.8488105Z             Args:
2025-03-04T20:59:08.8488237Z                 module (torch.nn.Module): Root module.
2025-03-04T20:59:08.8488494Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-03-04T20:59:08.8488730Z                 state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2025-03-04T20:59:08.8488907Z                     configuration for the target ``state_dict_type``.
2025-03-04T20:59:08.8489151Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2025-03-04T20:59:08.8489367Z                    ``state_dict`` configuration for the target ``state_dict_type``.
2025-03-04T20:59:08.8489457Z             
2025-03-04T20:59:08.8489729Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8489822Z     
2025-03-04T20:59:08.8489937Z       warnings.warn(msg)
2025-03-04T20:59:08.8490054Z     
2025-03-04T20:59:08.8490259Z --- Parse Warning: 66 / 116 ---
2025-03-04T20:59:08.8491470Z     /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=1810.
2025-03-04T20:59:08.8491751Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8491840Z     
2025-03-04T20:59:08.8492096Z     Transform the state-dict of an optimizer corresponding to a sharded model.
2025-03-04T20:59:08.8492183Z     
2025-03-04T20:59:08.8492396Z     The given state-dict can be transformed to one of three types:
2025-03-04T20:59:08.8492703Z     1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2025-03-04T20:59:08.8492802Z     
2025-03-04T20:59:08.8493044Z     For full optimizer state_dict, all states are unflattened and not sharded.
2025-03-04T20:59:08.8493279Z     Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2025-03-04T20:59:08.8493374Z     avoid OOM.
2025-03-04T20:59:08.8493471Z     
2025-03-04T20:59:08.8493721Z     For sharded optimizer state_dict, all states are unflattened but sharded.
2025-03-04T20:59:08.8494006Z     CPU only can be specified via :meth:`state_dict_type` to further save
2025-03-04T20:59:08.8494099Z     memory.
2025-03-04T20:59:08.8494203Z     
2025-03-04T20:59:08.8494429Z     For local state_dict, no transformation will be performed. But a state
2025-03-04T20:59:08.8494715Z     will be converted from nn.Tensor to ShardedTensor to represent its sharding
2025-03-04T20:59:08.8494840Z     nature (this is not supported yet).
2025-03-04T20:59:08.8494942Z     
2025-03-04T20:59:08.8495041Z     Example::
2025-03-04T20:59:08.8495145Z     
2025-03-04T20:59:08.8495287Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8495550Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:59:08.8495719Z         >>> from torch.distributed.fsdp import StateDictType
2025-03-04T20:59:08.8495922Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-04T20:59:08.8496135Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-04T20:59:08.8496256Z         >>> # Save a checkpoint
2025-03-04T20:59:08.8496366Z         >>> model, optim = ...
2025-03-04T20:59:08.8496496Z         >>> FSDP.set_state_dict_type(
2025-03-04T20:59:08.8496594Z         >>>     model,
2025-03-04T20:59:08.8496739Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:59:08.8496880Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8497046Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8497168Z         >>> )
2025-03-04T20:59:08.8497305Z         >>> state_dict = model.state_dict()
2025-03-04T20:59:08.8497488Z         >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-03-04T20:59:08.8497659Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-04T20:59:08.8497841Z         >>> # Load a checkpoint
2025-03-04T20:59:08.8497963Z         >>> model, optim = ...
2025-03-04T20:59:08.8498130Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-04T20:59:08.8498249Z         >>> FSDP.set_state_dict_type(
2025-03-04T20:59:08.8498357Z         >>>     model,
2025-03-04T20:59:08.8498487Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:59:08.8498638Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8498797Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8498901Z         >>> )
2025-03-04T20:59:08.8499030Z         >>> model.load_state_dict(state_dict)
2025-03-04T20:59:08.8499204Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-04T20:59:08.8499329Z         >>>     model, optim, optim_state_dict
2025-03-04T20:59:08.8499460Z         >>> )
2025-03-04T20:59:08.8499595Z         >>> optim.load_state_dict(optim_state_dict)
2025-03-04T20:59:08.8499696Z     
2025-03-04T20:59:08.8499788Z     Args:
2025-03-04T20:59:08.8500028Z         model (torch.nn.Module): Root module (which may or may not be a
2025-03-04T20:59:08.8500239Z             :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-04T20:59:08.8500392Z             were passed into the optimizer ``optim``.
2025-03-04T20:59:08.8500581Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-04T20:59:08.8500695Z             parameters.
2025-03-04T20:59:08.8500919Z         optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2025-03-04T20:59:08.8501145Z             transform. If the value is None, optim.state_dict() will be used. (
2025-03-04T20:59:08.8501250Z             Default: ``None``)
2025-03-04T20:59:08.8501511Z         group (dist.ProcessGroup): Model's process group across which parameters
2025-03-04T20:59:08.8501705Z             are sharded or ``None`` if using the default process group. (
2025-03-04T20:59:08.8501820Z             Default: ``None``)
2025-03-04T20:59:08.8501910Z     
2025-03-04T20:59:08.8502014Z     Returns:
2025-03-04T20:59:08.8502214Z         Dict[str, Any]: A :class:`dict` containing the optimizer state for
2025-03-04T20:59:08.8502401Z         ``model``. The sharding of the optimizer state is based on
2025-03-04T20:59:08.8502507Z         ``state_dict_type``.
2025-03-04T20:59:08.8502605Z     
2025-03-04T20:59:08.8502866Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8502965Z     
2025-03-04T20:59:08.8503095Z       warnings.warn(msg)
2025-03-04T20:59:08.8503196Z     
2025-03-04T20:59:08.8503404Z --- Parse Warning: 67 / 116 ---
2025-03-04T20:59:08.8504637Z     /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=1908.
2025-03-04T20:59:08.8504914Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8505013Z     
2025-03-04T20:59:08.8505378Z     Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2025-03-04T20:59:08.8505481Z     
2025-03-04T20:59:08.8505660Z     Given a ``optim_state_dict`` that is transformed through
2025-03-04T20:59:08.8505894Z     :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2025-03-04T20:59:08.8506117Z     state_dict that can be loaded to ``optim`` which is the optimizer for
2025-03-04T20:59:08.8506325Z     ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2025-03-04T20:59:08.8506412Z     
2025-03-04T20:59:08.8506595Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8506840Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-03-04T20:59:08.8507020Z         >>> from torch.distributed.fsdp import StateDictType
2025-03-04T20:59:08.8507207Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2025-03-04T20:59:08.8507428Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-03-04T20:59:08.8507533Z         >>> # Save a checkpoint
2025-03-04T20:59:08.8507652Z         >>> model, optim = ...
2025-03-04T20:59:08.8507769Z         >>> FSDP.set_state_dict_type(
2025-03-04T20:59:08.8507875Z         >>>     model,
2025-03-04T20:59:08.8508003Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:59:08.8508156Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8508310Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8508403Z         >>> )
2025-03-04T20:59:08.8508535Z         >>> state_dict = model.state_dict()
2025-03-04T20:59:08.8508662Z         >>> original_osd = optim.state_dict()
2025-03-04T20:59:08.8508813Z         >>> optim_state_dict = FSDP.optim_state_dict(
2025-03-04T20:59:08.8508937Z         >>>     model,
2025-03-04T20:59:08.8509044Z         >>>     optim,
2025-03-04T20:59:08.8509166Z         >>>     optim_state_dict=original_osd
2025-03-04T20:59:08.8509294Z         >>> )
2025-03-04T20:59:08.8509452Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-03-04T20:59:08.8509571Z         >>> # Load a checkpoint
2025-03-04T20:59:08.8509678Z         >>> model, optim = ...
2025-03-04T20:59:08.8509851Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-03-04T20:59:08.8509967Z         >>> FSDP.set_state_dict_type(
2025-03-04T20:59:08.8510076Z         >>>     model,
2025-03-04T20:59:08.8510204Z         >>>     StateDictType.FULL_STATE_DICT,
2025-03-04T20:59:08.8510355Z         >>>     FullStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8510510Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-03-04T20:59:08.8510611Z         >>> )
2025-03-04T20:59:08.8510736Z         >>> model.load_state_dict(state_dict)
2025-03-04T20:59:08.8510908Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-03-04T20:59:08.8511030Z         >>>     model, optim, optim_state_dict
2025-03-04T20:59:08.8511131Z         >>> )
2025-03-04T20:59:08.8511267Z         >>> optim.load_state_dict(optim_state_dict)
2025-03-04T20:59:08.8511368Z     
2025-03-04T20:59:08.8511460Z     Args:
2025-03-04T20:59:08.8511675Z         model (torch.nn.Module): Root module (which may or may not be a
2025-03-04T20:59:08.8511883Z             :class:`FullyShardedDataParallel` instance) whose parameters
2025-03-04T20:59:08.8512061Z             were passed into the optimizer ``optim``.
2025-03-04T20:59:08.8512251Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-03-04T20:59:08.8512363Z             parameters.
2025-03-04T20:59:08.8512585Z         optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2025-03-04T20:59:08.8512806Z         is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2025-03-04T20:59:08.8513009Z             KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2025-03-04T20:59:08.8513206Z             KeyedOptimizer or torch.distributed's NamedOptimizer.
2025-03-04T20:59:08.8513409Z         load_directly (bool): If this is set to True, this API will also
2025-03-04T20:59:08.8513628Z             call optim.load_state_dict(result) before returning the result.
2025-03-04T20:59:08.8513861Z             Otherwise, users are responsible to call ``optim.load_state_dict()``
2025-03-04T20:59:08.8513981Z             (Default: ``False``)
2025-03-04T20:59:08.8514230Z         group (dist.ProcessGroup): Model's process group across which parameters
2025-03-04T20:59:08.8514433Z             are sharded or ``None`` if using the default process group. (
2025-03-04T20:59:08.8514538Z             Default: ``None``)
2025-03-04T20:59:08.8514664Z     
2025-03-04T20:59:08.8514927Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8515030Z     
2025-03-04T20:59:08.8515135Z       warnings.warn(msg)
2025-03-04T20:59:08.8515221Z     
2025-03-04T20:59:08.8515430Z --- Parse Warning: 68 / 116 ---
2025-03-04T20:59:08.8516445Z     /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=128.
2025-03-04T20:59:08.8516716Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8516816Z     
2025-03-04T20:59:08.8517053Z     RemoteModule instance can only be created after RPC initialization.
2025-03-04T20:59:08.8517152Z     
2025-03-04T20:59:08.8517355Z     It creates a user-specified module on a specified remote node.
2025-03-04T20:59:08.8517604Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-04T20:59:08.8517719Z     executed on the remote node.
2025-03-04T20:59:08.8518000Z     It takes care of autograd recording to ensure the backward pass propagates
2025-03-04T20:59:08.8518164Z     gradients back to the corresponding remote module.
2025-03-04T20:59:08.8518589Z     It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2025-03-04T20:59:08.8518796Z     without incurring any overheads of copying the actual module,
2025-03-04T20:59:08.8519018Z     which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2025-03-04T20:59:08.8519133Z     pointing to the remote module.
2025-03-04T20:59:08.8519221Z     
2025-03-04T20:59:08.8519442Z     The arguments of ``forward_async`` and ``forward`` are the same as
2025-03-04T20:59:08.8519652Z     the ``forward`` method of the module returned by the ``module_cls``.
2025-03-04T20:59:08.8519749Z     
2025-03-04T20:59:08.8520069Z     Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2025-03-04T20:59:08.8520172Z     
2025-03-04T20:59:08.8520434Z     Particularly, to create a hybrid model, typically the local modules should be
2025-03-04T20:59:08.8520828Z     created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2025-03-04T20:59:08.8520931Z     Hybrid Example:
2025-03-04T20:59:08.8521071Z             >>> class HybridModel(nn.Module):
2025-03-04T20:59:08.8521195Z             >>>     def __init__(self) -> None:
2025-03-04T20:59:08.8521327Z             >>>         nn.Module.__init__(self)
2025-03-04T20:59:08.8521477Z             >>>         self.remote_embedding = RemoteModule(...)
2025-03-04T20:59:08.8521650Z             >>>         self.local_linear = nn.Linear(...)
2025-03-04T20:59:08.8521741Z     
2025-03-04T20:59:08.8521961Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-04T20:59:08.8522217Z     that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2025-03-04T20:59:08.8522446Z     the generated ``RemoteModule`` will have 2 methods in signature of
2025-03-04T20:59:08.8522589Z     ``def forward(input: Tensor) -> Tensor:`` and
2025-03-04T20:59:08.8522775Z     ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2025-03-04T20:59:08.8522863Z     
2025-03-04T20:59:08.8522975Z     .. note::
2025-03-04T20:59:08.8523128Z         If the remote module is placed on a cuda device,
2025-03-04T20:59:08.8523385Z         any input CPU tensors will be automatically moved to the same cuda device,
2025-03-04T20:59:08.8523794Z         and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2025-03-04T20:59:08.8523896Z     
2025-03-04T20:59:08.8523988Z     Args:
2025-03-04T20:59:08.8524303Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-04T20:59:08.8524603Z             The device can be a local device or a remote device specified by one of the following remote
2025-03-04T20:59:08.8524742Z             formats:
2025-03-04T20:59:08.8524834Z     
2025-03-04T20:59:08.8524999Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-04T20:59:08.8525161Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-04T20:59:08.8525264Z     
2025-03-04T20:59:08.8525520Z             In addition, the device field can be optional and the default value is "cpu".
2025-03-04T20:59:08.8525660Z         module_cls (nn.Module): For example,
2025-03-04T20:59:08.8525781Z             >>> class MyModule(nn.Module):
2025-03-04T20:59:08.8525909Z             >>>     def forward(input):
2025-03-04T20:59:08.8526020Z             >>>         return input + 1
2025-03-04T20:59:08.8526127Z             >>>
2025-03-04T20:59:08.8526238Z             >>> module_cls = MyModule
2025-03-04T20:59:08.8526463Z         args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-04T20:59:08.8526668Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-04T20:59:08.8526971Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-04T20:59:08.8527241Z             to be created. The type object should be decorated by @torch.jit.interface.
2025-03-04T20:59:08.8527510Z             If not provided, the generated RemoteModule is not torchscript-able.
2025-03-04T20:59:08.8527757Z             Warning, this is an experimental API and susceptible to frequent changes.
2025-03-04T20:59:08.8527860Z     
2025-03-04T20:59:08.8527954Z     Returns:
2025-03-04T20:59:08.8528222Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-04T20:59:08.8528465Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-04T20:59:08.8528763Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-04T20:59:08.8528916Z         on the user-provided module on the remote side.
2025-03-04T20:59:08.8529025Z     
2025-03-04T20:59:08.8529119Z     Example::
2025-03-04T20:59:08.8529295Z         Run the following code in two different processes:
2025-03-04T20:59:08.8529383Z     
2025-03-04T20:59:08.8529518Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8529616Z         >>> # On worker 0:
2025-03-04T20:59:08.8529730Z         >>> import torch
2025-03-04T20:59:08.8529862Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8529978Z         >>> from torch import nn, Tensor
2025-03-04T20:59:08.8530218Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-04T20:59:08.8530308Z         >>>
2025-03-04T20:59:08.8530465Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:59:08.8530623Z         >>> remote_linear_module = RemoteModule(
2025-03-04T20:59:08.8530770Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-04T20:59:08.8530862Z         >>> )
2025-03-04T20:59:08.8530990Z         >>> input = torch.randn(128, 20)
2025-03-04T20:59:08.8531153Z         >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-04T20:59:08.8531272Z         >>> ret = ret_fut.wait()
2025-03-04T20:59:08.8531377Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8531474Z     
2025-03-04T20:59:08.8531573Z         >>> # On worker 1:
2025-03-04T20:59:08.8531683Z         >>> import torch
2025-03-04T20:59:08.8531815Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8531918Z         >>>
2025-03-04T20:59:08.8532065Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:59:08.8532182Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8532270Z     
2025-03-04T20:59:08.8532546Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8532634Z     
2025-03-04T20:59:08.8532748Z       warnings.warn(msg)
2025-03-04T20:59:08.8532836Z     
2025-03-04T20:59:08.8533051Z --- Parse Warning: 69 / 116 ---
2025-03-04T20:59:08.8534153Z     /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=505.
2025-03-04T20:59:08.8534449Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8534539Z     
2025-03-04T20:59:08.8534886Z     Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2025-03-04T20:59:08.8534976Z     
2025-03-04T20:59:08.8535319Z     This alternate initialization method can be particularly useful if we want to create multiple
2025-03-04T20:59:08.8535649Z     RemoteModule instances that share the same underlying module and reduce memory consumption.
2025-03-04T20:59:08.8535752Z     
2025-03-04T20:59:08.8536039Z     Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2025-03-04T20:59:08.8536246Z     which is not supported. The recommended way is as follows:
2025-03-04T20:59:08.8536335Z     
2025-03-04T20:59:08.8536476Z         1. the sender creates a RemoteModule;
2025-03-04T20:59:08.8536658Z         2. the sender sends its ``module_rref`` over RPC;
2025-03-04T20:59:08.8537052Z         3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2025-03-04T20:59:08.8537143Z     
2025-03-04T20:59:08.8537251Z     Example::
2025-03-04T20:59:08.8537413Z         Run the following code in two different processes:
2025-03-04T20:59:08.8537513Z     
2025-03-04T20:59:08.8537636Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8537819Z         >>> # On worker 0:
2025-03-04T20:59:08.8537922Z         >>> import torch
2025-03-04T20:59:08.8538060Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8538190Z         >>> from torch import nn, Tensor
2025-03-04T20:59:08.8538422Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-04T20:59:08.8538526Z         >>>
2025-03-04T20:59:08.8538670Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:59:08.8538806Z         >>> remote_module = RemoteModule(
2025-03-04T20:59:08.8538943Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-04T20:59:08.8539051Z         >>> )
2025-03-04T20:59:08.8539142Z         >>>
2025-03-04T20:59:08.8539278Z         >>> remote_module1 = rpc.rpc_sync(
2025-03-04T20:59:08.8539384Z         >>>     "worker1/cpu",
2025-03-04T20:59:08.8539529Z         >>>     RemoteModule.init_from_module_rref,
2025-03-04T20:59:08.8539695Z         >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2025-03-04T20:59:08.8539795Z         >>> )
2025-03-04T20:59:08.8539900Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8540031Z     
2025-03-04T20:59:08.8540133Z         >>> # On worker 1:
2025-03-04T20:59:08.8540245Z         >>> import torch
2025-03-04T20:59:08.8540377Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8540481Z         >>>
2025-03-04T20:59:08.8540628Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:59:08.8540743Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8540831Z     
2025-03-04T20:59:08.8540933Z     Args:
2025-03-04T20:59:08.8541238Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-04T20:59:08.8541546Z             The device can be a local device or a remote device specified by one of the following remote
2025-03-04T20:59:08.8541639Z             formats:
2025-03-04T20:59:08.8541742Z     
2025-03-04T20:59:08.8541891Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-03-04T20:59:08.8542072Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-03-04T20:59:08.8542162Z     
2025-03-04T20:59:08.8542430Z             In addition, the device field can be optional and the default value is "cpu".
2025-03-04T20:59:08.8542687Z         module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2025-03-04T20:59:08.8542845Z             the created remote module.
2025-03-04T20:59:08.8543132Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-03-04T20:59:08.8543385Z             to be created. The type object should be decorated by @torch.jit.interface.
2025-03-04T20:59:08.8543615Z             If not provided, the generated RemoteModule is not torchscript-able.
2025-03-04T20:59:08.8543870Z             Warning, this is an experimental API and susceptible to frequent changes.
2025-03-04T20:59:08.8543959Z     
2025-03-04T20:59:08.8544065Z     Returns:
2025-03-04T20:59:08.8544319Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-04T20:59:08.8544577Z         user-provided ``module_rref``, it has a blocking ``forward`` method and an
2025-03-04T20:59:08.8544859Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-04T20:59:08.8545022Z         on the user-provided module on the remote side.
2025-03-04T20:59:08.8545113Z     
2025-03-04T20:59:08.8545374Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8545502Z     
2025-03-04T20:59:08.8545609Z       warnings.warn(msg)
2025-03-04T20:59:08.8545707Z     
2025-03-04T20:59:08.8545916Z --- Parse Warning: 70 / 116 ---
2025-03-04T20:59:08.8546937Z     /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=597.
2025-03-04T20:59:08.8547209Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8547311Z     
2025-03-04T20:59:08.8547550Z         A RemoteModule instance can only be created after RPC initialization.
2025-03-04T20:59:08.8547649Z     
2025-03-04T20:59:08.8547852Z         It creates a user-specified module on a specified remote node.
2025-03-04T20:59:08.8548109Z         It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-03-04T20:59:08.8548225Z         executed on the remote node.
2025-03-04T20:59:08.8548482Z         It takes care of autograd recording to ensure the backward pass propagates
2025-03-04T20:59:08.8548651Z         gradients back to the corresponding remote module.
2025-03-04T20:59:08.8548751Z     
2025-03-04T20:59:08.8548980Z         It generates two methods ``forward_async`` and ``forward`` based on the
2025-03-04T20:59:08.8549214Z         signature of the ``forward`` method of ``module_cls``. ``forward_async``
2025-03-04T20:59:08.8549474Z         runs asynchronously and returns a Future. The arguments of ``forward_async``
2025-03-04T20:59:08.8549720Z         and ``forward`` are the same as the ``forward`` method of the module
2025-03-04T20:59:08.8549845Z         returned by the ``module_cls``.
2025-03-04T20:59:08.8549946Z     
2025-03-04T20:59:08.8550155Z         For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-03-04T20:59:08.8550429Z         that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2025-03-04T20:59:08.8550667Z         the generated ``RemoteModule`` will have 2 methods with the signatures:
2025-03-04T20:59:08.8550769Z     
2025-03-04T20:59:08.8550908Z         | ``def forward(input: Tensor) -> Tensor:``
2025-03-04T20:59:08.8551096Z         | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2025-03-04T20:59:08.8551184Z     
2025-03-04T20:59:08.8551288Z     Args:
2025-03-04T20:59:08.8551594Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-03-04T20:59:08.8551960Z             The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2025-03-04T20:59:08.8552112Z             E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2025-03-04T20:59:08.8552380Z             In addition, the device field can be optional and the default value is "cpu".
2025-03-04T20:59:08.8552663Z         module_cls (nn.Module): Class for the module to be created remotely. For example,
2025-03-04T20:59:08.8552766Z     
2025-03-04T20:59:08.8552885Z             >>> class MyModule(nn.Module):
2025-03-04T20:59:08.8553007Z             >>>     def forward(input):
2025-03-04T20:59:08.8553118Z             >>>         return input + 1
2025-03-04T20:59:08.8553221Z             >>>
2025-03-04T20:59:08.8553328Z             >>> module_cls = MyModule
2025-03-04T20:59:08.8553426Z     
2025-03-04T20:59:08.8553634Z         args (Sequence, optional): args to be passed to ``module_cls``.
2025-03-04T20:59:08.8553846Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-03-04T20:59:08.8553935Z     
2025-03-04T20:59:08.8554039Z     Returns:
2025-03-04T20:59:08.8554289Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-03-04T20:59:08.8554549Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-03-04T20:59:08.8554834Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-03-04T20:59:08.8555045Z         on the user-provided module on the remote side.
2025-03-04T20:59:08.8555137Z     
2025-03-04T20:59:08.8555252Z     Example::
2025-03-04T20:59:08.8555417Z         Run the following code in two different processes:
2025-03-04T20:59:08.8555552Z     
2025-03-04T20:59:08.8555681Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8555800Z         >>> # On worker 0:
2025-03-04T20:59:08.8555903Z         >>> import torch
2025-03-04T20:59:08.8556053Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8556171Z         >>> from torch import nn, Tensor
2025-03-04T20:59:08.8556418Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-03-04T20:59:08.8556513Z         >>>
2025-03-04T20:59:08.8556669Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:59:08.8556819Z         >>> remote_linear_module = RemoteModule(
2025-03-04T20:59:08.8556959Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-03-04T20:59:08.8557069Z         >>> )
2025-03-04T20:59:08.8557187Z         >>> input = torch.randn(128, 20)
2025-03-04T20:59:08.8557369Z         >>> ret_fut = remote_linear_module.forward_async(input)
2025-03-04T20:59:08.8557481Z         >>> ret = ret_fut.wait()
2025-03-04T20:59:08.8557603Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8557694Z     
2025-03-04T20:59:08.8557811Z         >>> # On worker 1:
2025-03-04T20:59:08.8557913Z         >>> import torch
2025-03-04T20:59:08.8558066Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8558159Z         >>>
2025-03-04T20:59:08.8558318Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:59:08.8558450Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8558553Z     
2025-03-04T20:59:08.8558758Z         Furthermore, a more practical example that is combined with
2025-03-04T20:59:08.8559258Z         `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2025-03-04T20:59:08.8559603Z         can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2025-03-04T20:59:08.8559709Z     
2025-03-04T20:59:08.8559972Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8560075Z     
2025-03-04T20:59:08.8560180Z       warnings.warn(msg)
2025-03-04T20:59:08.8560281Z     
2025-03-04T20:59:08.8560483Z --- Parse Warning: 71 / 116 ---
2025-03-04T20:59:08.8561497Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130.
2025-03-04T20:59:08.8561769Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8561871Z     
2025-03-04T20:59:08.8562155Z     DistributedOptimizer takes remote references to parameters scattered
2025-03-04T20:59:08.8562417Z     across workers and applies the given optimizer locally for each parameter.
2025-03-04T20:59:08.8562507Z     
2025-03-04T20:59:08.8562764Z     This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2025-03-04T20:59:08.8562929Z     to retrieve the gradients for specific parameters.
2025-03-04T20:59:08.8563028Z     
2025-03-04T20:59:08.8563132Z     Concurrent calls to
2025-03-04T20:59:08.8563361Z     :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2025-03-04T20:59:08.8563513Z     either from the same or different clients, will
2025-03-04T20:59:08.8563756Z     be serialized on each worker -- as each worker's optimizer can only work
2025-03-04T20:59:08.8563971Z     on one set of gradients at a time. However, there is no guarantee that
2025-03-04T20:59:08.8564239Z     the full forward-backward-optimizer sequence will execute for one client
2025-03-04T20:59:08.8564467Z     at a time. This means that the gradients being applied may not correspond
2025-03-04T20:59:08.8564712Z     to the latest forward pass executed on a given worker. Also, there is no
2025-03-04T20:59:08.8564866Z     guaranteed ordering across workers.
2025-03-04T20:59:08.8564965Z     
2025-03-04T20:59:08.8565261Z     `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2025-03-04T20:59:08.8565512Z     by default, so that optimizer updates are not blocked by the Python Global
2025-03-04T20:59:08.8565766Z     Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2025-03-04T20:59:08.8566028Z     Model Parallel). This feature is currently enabled for most optimizers. You
2025-03-04T20:59:08.8566290Z     can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2025-03-04T20:59:08.8566419Z     for your own custom optimizers.
2025-03-04T20:59:08.8566509Z     
2025-03-04T20:59:08.8566612Z     Args:
2025-03-04T20:59:08.8566817Z         optimizer_class (optim.Optimizer): the class of optimizer to
2025-03-04T20:59:08.8566951Z             instantiate on each worker.
2025-03-04T20:59:08.8567170Z         params_rref (list[RRef]): list of RRefs to local or remote parameters
2025-03-04T20:59:08.8567283Z             to optimize.
2025-03-04T20:59:08.8567507Z         args: arguments to pass to the optimizer constructor on each worker.
2025-03-04T20:59:08.8567752Z         kwargs: arguments to pass to the optimizer constructor on each worker.
2025-03-04T20:59:08.8567841Z     
2025-03-04T20:59:08.8567941Z     Example::
2025-03-04T20:59:08.8568079Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8568253Z         >>> import torch.distributed.autograd as dist_autograd
2025-03-04T20:59:08.8568427Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8568542Z         >>> from torch import optim
2025-03-04T20:59:08.8568751Z         >>> from torch.distributed.optim import DistributedOptimizer
2025-03-04T20:59:08.8568844Z         >>>
2025-03-04T20:59:08.8569000Z         >>> with dist_autograd.context() as context_id:
2025-03-04T20:59:08.8569107Z         >>>   # Forward pass.
2025-03-04T20:59:08.8569331Z         >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2025-03-04T20:59:08.8569537Z         >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2025-03-04T20:59:08.8569685Z         >>>   loss = rref1.to_here() + rref2.to_here()
2025-03-04T20:59:08.8569777Z         >>>
2025-03-04T20:59:08.8569896Z         >>>   # Backward pass.
2025-03-04T20:59:08.8570059Z         >>>   dist_autograd.backward(context_id, [loss.sum()])
2025-03-04T20:59:08.8570162Z         >>>
2025-03-04T20:59:08.8570264Z         >>>   # Optimizer.
2025-03-04T20:59:08.8570411Z         >>>   dist_optim = DistributedOptimizer(
2025-03-04T20:59:08.8570511Z         >>>      optim.SGD,
2025-03-04T20:59:08.8570627Z         >>>      [rref1, rref2],
2025-03-04T20:59:08.8570724Z         >>>      lr=0.05,
2025-03-04T20:59:08.8570854Z         >>>   )
2025-03-04T20:59:08.8570974Z         >>>   dist_optim.step(context_id)
2025-03-04T20:59:08.8571076Z     
2025-03-04T20:59:08.8571242Z     __ https://github.com/pytorch/tutorials/pull/1465
2025-03-04T20:59:08.8571343Z     
2025-03-04T20:59:08.8571606Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8571705Z     
2025-03-04T20:59:08.8571812Z       warnings.warn(msg)
2025-03-04T20:59:08.8571912Z     
2025-03-04T20:59:08.8572109Z --- Parse Warning: 72 / 116 ---
2025-03-04T20:59:08.8573197Z     /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.
2025-03-04T20:59:08.8573469Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8573772Z     
2025-03-04T20:59:08.8574216Z     Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-03-04T20:59:08.8574397Z     This optimizer runs local optimizer at every step.
2025-03-04T20:59:08.8574826Z     After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
2025-03-04T20:59:08.8574928Z     
2025-03-04T20:59:08.8575020Z     Args:
2025-03-04T20:59:08.8575187Z         optim: The local optimizer.
2025-03-04T20:59:08.8575419Z         averager: A model averager instance to run post-localSGD algorithm.
2025-03-04T20:59:08.8575521Z     
2025-03-04T20:59:08.8575624Z     Example::
2025-03-04T20:59:08.8575710Z     
2025-03-04T20:59:08.8575859Z         >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8575961Z         >>> import torch
2025-03-04T20:59:08.8576103Z         >>> import torch.distributed as dist
2025-03-04T20:59:08.8576384Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-03-04T20:59:08.8576510Z         >>> import torch.nn as nn
2025-03-04T20:59:08.8576714Z         >>> from torch.distributed.optim import PostLocalSGDOptimizer
2025-03-04T20:59:08.8577011Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-03-04T20:59:08.8577125Z         >>>   PostLocalSGDState,
2025-03-04T20:59:08.8577246Z         >>>   post_localSGD_hook,
2025-03-04T20:59:08.8577338Z         >>> )
2025-03-04T20:59:08.8577438Z         >>>
2025-03-04T20:59:08.8577600Z         >>> model = nn.parallel.DistributedDataParallel(
2025-03-04T20:59:08.8577832Z         >>>    module, device_ids=[rank], output_device=rank
2025-03-04T20:59:08.8577923Z         >>> )
2025-03-04T20:59:08.8578030Z         >>>
2025-03-04T20:59:08.8578225Z         >>> # Register a post-localSGD communication hook.
2025-03-04T20:59:08.8578548Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-03-04T20:59:08.8578721Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-03-04T20:59:08.8578824Z         >>>
2025-03-04T20:59:08.8579037Z         >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2025-03-04T20:59:08.8579309Z         >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2025-03-04T20:59:08.8579484Z         >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-03-04T20:59:08.8579720Z         >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2025-03-04T20:59:08.8579846Z         >>> opt = PostLocalSGDOptimizer(
2025-03-04T20:59:08.8579969Z         >>>     optim=local_optim,
2025-03-04T20:59:08.8580228Z         >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-03-04T20:59:08.8580331Z         >>> )
2025-03-04T20:59:08.8580422Z         >>>
2025-03-04T20:59:08.8580667Z         >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2025-03-04T20:59:08.8580978Z         >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2025-03-04T20:59:08.8581414Z         >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2025-03-04T20:59:08.8581534Z         >>> for step in range(0, 200):
2025-03-04T20:59:08.8581655Z         >>>    opt.zero_grad()
2025-03-04T20:59:08.8581782Z         >>>    loss = loss_fn(output, labels)
2025-03-04T20:59:08.8581901Z         >>>    loss.backward()
2025-03-04T20:59:08.8582002Z         >>>    opt.step()
2025-03-04T20:59:08.8582106Z     
2025-03-04T20:59:08.8582370Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8582469Z     
2025-03-04T20:59:08.8582577Z       warnings.warn(msg)
2025-03-04T20:59:08.8582678Z     
2025-03-04T20:59:08.8582904Z --- Parse Warning: 73 / 116 ---
2025-03-04T20:59:08.8584022Z     /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.
2025-03-04T20:59:08.8584328Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8584430Z     
2025-03-04T20:59:08.8584867Z     Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2025-03-04T20:59:08.8584968Z     
2025-03-04T20:59:08.8585105Z     The sharing is done as described by ZeRO_.
2025-03-04T20:59:08.8585205Z     
2025-03-04T20:59:08.8585362Z     The local optimizer instance in each rank is only
2025-03-04T20:59:08.8585628Z     responsible for updating approximately ``1 / world_size`` parameters and
2025-03-04T20:59:08.8585843Z     hence only needs to keep ``1 / world_size`` optimizer states. After
2025-03-04T20:59:08.8586110Z     parameters are updated locally, each rank will broadcast its parameters to
2025-03-04T20:59:08.8586308Z     all other peers to keep all model replicas in the same state.
2025-03-04T20:59:08.8586530Z     ``ZeroRedundancyOptimizer`` can be used in conjunction with
2025-03-04T20:59:08.8586799Z     :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2025-03-04T20:59:08.8586922Z     memory consumption.
2025-03-04T20:59:08.8587010Z     
2025-03-04T20:59:08.8587300Z     ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2025-03-04T20:59:08.8587545Z     of parameters at each rank. Each parameter belongs to a single rank and is
2025-03-04T20:59:08.8587809Z     not divided among ranks. The partition is arbitrary and might not match the
2025-03-04T20:59:08.8587951Z     the parameter registration or usage order.
2025-03-04T20:59:08.8588055Z     
2025-03-04T20:59:08.8588153Z     Arguments:
2025-03-04T20:59:08.8588383Z         params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2025-03-04T20:59:08.8588596Z             or :class:`dict` s giving all parameters, which will be sharded
2025-03-04T20:59:08.8588701Z             across ranks.
2025-03-04T20:59:08.8588802Z     
2025-03-04T20:59:08.8588904Z     Keyword Args:
2025-03-04T20:59:08.8589156Z         optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2025-03-04T20:59:08.8589256Z             optimizer.
2025-03-04T20:59:08.8589490Z         process_group (``ProcessGroup``, optional): ``torch.distributed``
2025-03-04T20:59:08.8589700Z             ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2025-03-04T20:59:08.8589880Z             :meth:`torch.distributed.init_process_group`).
2025-03-04T20:59:08.8590119Z         parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2025-03-04T20:59:08.8590356Z             packed into buckets to speed up communication, and ``param.data``
2025-03-04T20:59:08.8590571Z             fields point to bucket views at different offsets; if ``False``,
2025-03-04T20:59:08.8590804Z             each individual parameter is communicated separately, and each
2025-03-04T20:59:08.8591014Z             ``params.data`` stays intact (default: ``False``).
2025-03-04T20:59:08.8591234Z         overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2025-03-04T20:59:08.8591446Z             overlapped with :class:`DistributedDataParallel` 's gradient
2025-03-04T20:59:08.8591685Z             synchronization; this requires (1) either a functional optimizer
2025-03-04T20:59:08.8591881Z             for the ``optimizer_class`` argument or one with a functional
2025-03-04T20:59:08.8592082Z             equivalent and (2) registering a DDP communication hook
2025-03-04T20:59:08.8592291Z             constructed from one of the functions in ``ddp_zero_hook.py``;
2025-03-04T20:59:08.8592479Z             parameters are packed into buckets matching those in
2025-03-04T20:59:08.8592650Z             :class:`DistributedDataParallel`, meaning that the
2025-03-04T20:59:08.8592824Z             ``parameters_as_bucket_view`` argument is ignored.
2025-03-04T20:59:08.8593021Z             If ``False``, :meth:`step` runs disjointly after the backward pass
2025-03-04T20:59:08.8593139Z             (per normal).
2025-03-04T20:59:08.8593248Z             (default: ``False``)
2025-03-04T20:59:08.8593508Z         **defaults: any trailing arguments, which are forwarded to the local
2025-03-04T20:59:08.8593605Z             optimizer.
2025-03-04T20:59:08.8593702Z     
2025-03-04T20:59:08.8593829Z     Example::
2025-03-04T20:59:08.8593928Z     
2025-03-04T20:59:08.8594035Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8594159Z         >>> import torch.nn as nn
2025-03-04T20:59:08.8594371Z         >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2025-03-04T20:59:08.8594592Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-03-04T20:59:08.8594831Z         >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2025-03-04T20:59:08.8594973Z         >>> ddp = DDP(model, device_ids=[rank])
2025-03-04T20:59:08.8595101Z         >>> opt = ZeroRedundancyOptimizer(
2025-03-04T20:59:08.8595224Z         >>>     ddp.parameters(),
2025-03-04T20:59:08.8595355Z         >>>     optimizer_class=torch.optim.Adam,
2025-03-04T20:59:08.8595466Z         >>>     lr=0.01
2025-03-04T20:59:08.8595557Z         >>> )
2025-03-04T20:59:08.8595687Z         >>> ddp(inputs).sum().backward()
2025-03-04T20:59:08.8595787Z         >>> opt.step()
2025-03-04T20:59:08.8595887Z     
2025-03-04T20:59:08.8595985Z     .. warning::
2025-03-04T20:59:08.8596218Z         Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2025-03-04T20:59:08.8596373Z         passed-in parameters are the same dense type.
2025-03-04T20:59:08.8596474Z     
2025-03-04T20:59:08.8596568Z     .. warning::
2025-03-04T20:59:08.8596831Z         If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2025-03-04T20:59:08.8597045Z         the way that overlapping :class:`DistributedDataParallel` with
2025-03-04T20:59:08.8597302Z         :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2025-03-04T20:59:08.8597530Z         two or three training iterations do not perform parameter updates in
2025-03-04T20:59:08.8597745Z         the optimizer step, depending on if ``static_graph=False`` or
2025-03-04T20:59:08.8597942Z         ``static_graph=True``, respectively. This is because it needs
2025-03-04T20:59:08.8598152Z         information about the gradient bucketing strategy used by
2025-03-04T20:59:08.8598383Z         :class:`DistributedDataParallel`, which is not finalized until the
2025-03-04T20:59:08.8598606Z         second forward pass if ``static_graph=False`` or until the third
2025-03-04T20:59:08.8598823Z         forward pass if ``static_graph=True``. To adjust for this, one option
2025-03-04T20:59:08.8598953Z         is to prepend dummy inputs.
2025-03-04T20:59:08.8599044Z     
2025-03-04T20:59:08.8599322Z     .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2025-03-04T20:59:08.8599410Z     
2025-03-04T20:59:08.8599553Z     .. _ZeRO: https://arxiv.org/abs/1910.02054
2025-03-04T20:59:08.8599681Z     
2025-03-04T20:59:08.8599769Z     
2025-03-04T20:59:08.8600045Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8600134Z     
2025-03-04T20:59:08.8600251Z       warnings.warn(msg)
2025-03-04T20:59:08.8600338Z     
2025-03-04T20:59:08.8600555Z --- Parse Warning: 74 / 116 ---
2025-03-04T20:59:08.8601551Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28.
2025-03-04T20:59:08.8601839Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8601928Z     
2025-03-04T20:59:08.8602187Z     Custom reducer class that can be used to specify a custom operation that
2025-03-04T20:59:08.8602375Z     reduces losses of multiple microbatches into one value.
2025-03-04T20:59:08.8602477Z     
2025-03-04T20:59:08.8602573Z     Example:
2025-03-04T20:59:08.8602690Z     >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8602812Z     >>> sum_reducer = _CustomReducer(
2025-03-04T20:59:08.8602962Z     >>>     torch.tensor(0.0),
2025-03-04T20:59:08.8603067Z     >>>     lambda a, b: a + b
2025-03-04T20:59:08.8603174Z     >>> )
2025-03-04T20:59:08.8603263Z     
2025-03-04T20:59:08.8603563Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8603651Z     
2025-03-04T20:59:08.8603769Z       warnings.warn(msg)
2025-03-04T20:59:08.8603857Z     
2025-03-04T20:59:08.8604062Z --- Parse Warning: 75 / 116 ---
2025-03-04T20:59:08.8604992Z     /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.
2025-03-04T20:59:08.8605275Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8605364Z     
2025-03-04T20:59:08.8605625Z     A decorator for a function indicating that the return value of the function
2025-03-04T20:59:08.8605845Z     is guaranteed to be a :class:`~torch.futures.Future` object and this
2025-03-04T20:59:08.8606106Z     function can run asynchronously on the RPC callee. More specifically, the
2025-03-04T20:59:08.8606353Z     callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2025-03-04T20:59:08.8606610Z     function and installs subsequent processing steps as a callback to that
2025-03-04T20:59:08.8606852Z     :class:`~torch.futures.Future`. The installed callback will read the value
2025-03-04T20:59:08.8607074Z     from the :class:`~torch.futures.Future` when completed and send the
2025-03-04T20:59:08.8607290Z     value back as the RPC response. That also means the returned
2025-03-04T20:59:08.8607541Z     :class:`~torch.futures.Future` only exists on the callee side and is never
2025-03-04T20:59:08.8607774Z     sent through RPC. This decorator is useful when the wrapped function's
2025-03-04T20:59:08.8607992Z     (``fn``) execution needs to pause and resume due to, e.g., containing
2025-03-04T20:59:08.8608229Z     :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2025-03-04T20:59:08.8608333Z     
2025-03-04T20:59:08.8608555Z     .. note:: To enable asynchronous execution, applications must pass the
2025-03-04T20:59:08.8608810Z         function object returned by this decorator to RPC APIs. If RPC detected
2025-03-04T20:59:08.8609037Z         attributes installed by this decorator, it knows that this function
2025-03-04T20:59:08.8609241Z         returns a ``Future`` object and will handle that accordingly.
2025-03-04T20:59:08.8609468Z         However, this does not mean this decorator has to be outmost one when
2025-03-04T20:59:08.8609714Z         defining a function. For example, when combined with ``@staticmethod``
2025-03-04T20:59:08.8609933Z         or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2025-03-04T20:59:08.8610204Z         inner decorator to allow the target function be recognized as a static
2025-03-04T20:59:08.8610443Z         or class function. This target function can still execute asynchronously
2025-03-04T20:59:08.8610691Z         because, when accessed, the static or class method preserves attributes
2025-03-04T20:59:08.8610860Z         installed by ``@rpc.functions.async_execution``.
2025-03-04T20:59:08.8610962Z     
2025-03-04T20:59:08.8611050Z     
2025-03-04T20:59:08.8611157Z     Example::
2025-03-04T20:59:08.8611366Z         The returned :class:`~torch.futures.Future` object can come from
2025-03-04T20:59:08.8611520Z         :meth:`~torch.distributed.rpc.rpc_async`,
2025-03-04T20:59:08.8611754Z         :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2025-03-04T20:59:08.8611952Z         constructor. The example below shows directly using the
2025-03-04T20:59:08.8612093Z         :class:`~torch.futures.Future` returned by
2025-03-04T20:59:08.8612238Z         :meth:`~torch.futures.Future.then`.
2025-03-04T20:59:08.8612328Z     
2025-03-04T20:59:08.8612470Z         >>> from torch.distributed import rpc
2025-03-04T20:59:08.8612587Z         >>>
2025-03-04T20:59:08.8612724Z         >>> # omitting setup and shutdown RPC
2025-03-04T20:59:08.8612815Z         >>>
2025-03-04T20:59:08.8612929Z         >>> # On all workers
2025-03-04T20:59:08.8613078Z         >>> @rpc.functions.async_execution
2025-03-04T20:59:08.8613204Z         >>> def async_add_chained(to, x, y, z):
2025-03-04T20:59:08.8613419Z         >>>     # This function runs on "worker1" and returns immediately when
2025-03-04T20:59:08.8613617Z         >>>     # the callback is installed through the `then(cb)` API. In the
2025-03-04T20:59:08.8613825Z         >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2025-03-04T20:59:08.8613993Z         >>>     # When the return value of that `rpc_async` arrives at
2025-03-04T20:59:08.8614206Z         >>>     # "worker1", "worker1" will run the lambda function accordingly
2025-03-04T20:59:08.8614406Z         >>>     # and set the value for the previously returned `Future`, which
2025-03-04T20:59:08.8614613Z         >>>     # will then trigger RPC to send the result back to "worker0".
2025-03-04T20:59:08.8614805Z         >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:08.8614933Z         >>>         lambda fut: fut.wait() + z
2025-03-04T20:59:08.8615027Z         >>>     )
2025-03-04T20:59:08.8615132Z         >>>
2025-03-04T20:59:08.8615229Z         >>> # On worker0
2025-03-04T20:59:08.8615350Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8615458Z         >>> ret = rpc.rpc_sync(
2025-03-04T20:59:08.8615569Z         >>>     "worker1",
2025-03-04T20:59:08.8616286Z         >>>     async_add_chained,
2025-03-04T20:59:08.8616429Z         >>>     args=("worker2", torch.ones(2), 1, 1)
2025-03-04T20:59:08.8616520Z         >>> )
2025-03-04T20:59:08.8616660Z         >>> print(ret)  # prints tensor([3., 3.])
2025-03-04T20:59:08.8616751Z     
2025-03-04T20:59:08.8616999Z         When combined with TorchScript decorators, this decorator must be the
2025-03-04T20:59:08.8617108Z         outmost one.
2025-03-04T20:59:08.8617208Z     
2025-03-04T20:59:08.8617322Z         >>> from torch import Tensor
2025-03-04T20:59:08.8617461Z         >>> from torch.futures import Future
2025-03-04T20:59:08.8617593Z         >>> from torch.distributed import rpc
2025-03-04T20:59:08.8617698Z         >>>
2025-03-04T20:59:08.8617911Z         >>> # omitting setup and shutdown RPC
2025-03-04T20:59:08.8618020Z         >>>
2025-03-04T20:59:08.8618124Z         >>> # On all workers
2025-03-04T20:59:08.8618246Z         >>> @torch.jit.script
2025-03-04T20:59:08.8618402Z         >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2025-03-04T20:59:08.8618518Z         >>>     return x + y
2025-03-04T20:59:08.8618609Z         >>>
2025-03-04T20:59:08.8618746Z         >>> @rpc.functions.async_execution
2025-03-04T20:59:08.8618853Z         >>> @torch.jit.script
2025-03-04T20:59:08.8619098Z         >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2025-03-04T20:59:08.8619251Z         >>>     return rpc.rpc_async(to, script_add, (x, y))
2025-03-04T20:59:08.8619355Z         >>>
2025-03-04T20:59:08.8619454Z         >>> # On worker0
2025-03-04T20:59:08.8619563Z         >>> ret = rpc.rpc_sync(
2025-03-04T20:59:08.8619681Z         >>>     "worker1",
2025-03-04T20:59:08.8619782Z         >>>     async_add,
2025-03-04T20:59:08.8619923Z         >>>     args=("worker2", torch.ones(2), 1)
2025-03-04T20:59:08.8620014Z         >>> )
2025-03-04T20:59:08.8620157Z         >>> print(ret)  # prints tensor([2., 2.])
2025-03-04T20:59:08.8620247Z     
2025-03-04T20:59:08.8620494Z         When combined with static or class method, this decorator must be the
2025-03-04T20:59:08.8620591Z         inner one.
2025-03-04T20:59:08.8620692Z     
2025-03-04T20:59:08.8620820Z         >>> from torch.distributed import rpc
2025-03-04T20:59:08.8620925Z         >>>
2025-03-04T20:59:08.8621053Z         >>> # omitting setup and shutdown RPC
2025-03-04T20:59:08.8621156Z         >>>
2025-03-04T20:59:08.8621346Z         >>> # On all workers
2025-03-04T20:59:08.8621484Z         >>> class AsyncExecutionClass:
2025-03-04T20:59:08.8621575Z         >>>
2025-03-04T20:59:08.8621689Z         >>>     @staticmethod
2025-03-04T20:59:08.8621840Z         >>>     @rpc.functions.async_execution
2025-03-04T20:59:08.8621985Z         >>>     def static_async_add(to, x, y, z):
2025-03-04T20:59:08.8622165Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:08.8622299Z         >>>             lambda fut: fut.wait() + z
2025-03-04T20:59:08.8622395Z         >>>         )
2025-03-04T20:59:08.8622500Z         >>>
2025-03-04T20:59:08.8622603Z         >>>     @classmethod
2025-03-04T20:59:08.8622739Z         >>>     @rpc.functions.async_execution
2025-03-04T20:59:08.8622871Z         >>>     def class_async_add(cls, to, x, y, z):
2025-03-04T20:59:08.8623017Z         >>>         ret_fut = torch.futures.Future()
2025-03-04T20:59:08.8623176Z         >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:08.8623347Z         >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2025-03-04T20:59:08.8623440Z         >>>         )
2025-03-04T20:59:08.8623555Z         >>>         return ret_fut
2025-03-04T20:59:08.8623644Z         >>>
2025-03-04T20:59:08.8623772Z         >>>     @rpc.functions.async_execution
2025-03-04T20:59:08.8623916Z         >>>     def bound_async_add(self, to, x, y, z):
2025-03-04T20:59:08.8624093Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-03-04T20:59:08.8624225Z         >>>             lambda fut: fut.wait() + z
2025-03-04T20:59:08.8624317Z         >>>         )
2025-03-04T20:59:08.8624460Z         >>>
2025-03-04T20:59:08.8624559Z         >>> # On worker0
2025-03-04T20:59:08.8624677Z         >>> ret = rpc.rpc_sync(
2025-03-04T20:59:08.8624780Z         >>>     "worker1",
2025-03-04T20:59:08.8624936Z         >>>     AsyncExecutionClass.static_async_add,
2025-03-04T20:59:08.8625064Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-04T20:59:08.8625168Z         >>> )
2025-03-04T20:59:08.8625297Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:08.8625402Z         >>>
2025-03-04T20:59:08.8625509Z         >>> ret = rpc.rpc_sync(
2025-03-04T20:59:08.8625621Z         >>>     "worker1",
2025-03-04T20:59:08.8625763Z         >>>     AsyncExecutionClass.class_async_add,
2025-03-04T20:59:08.8625904Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2025-03-04T20:59:08.8625996Z         >>> )
2025-03-04T20:59:08.8626138Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:08.8626228Z     
2025-03-04T20:59:08.8626408Z         This decorator also works with RRef helpers, i.e., .
2025-03-04T20:59:08.8626559Z         :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2025-03-04T20:59:08.8626734Z         :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2025-03-04T20:59:08.8626908Z         :meth:`torch.distributed.rpc.RRef.remote`.
2025-03-04T20:59:08.8627006Z     
2025-03-04T20:59:08.8627135Z         >>> from torch.distributed import rpc
2025-03-04T20:59:08.8627239Z         >>>
2025-03-04T20:59:08.8627385Z         >>> # reuse the AsyncExecutionClass class above
2025-03-04T20:59:08.8627559Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-04T20:59:08.8627778Z         >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2025-03-04T20:59:08.8627914Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:08.8628003Z         >>>
2025-03-04T20:59:08.8628175Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-04T20:59:08.8628418Z         >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2025-03-04T20:59:08.8628559Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:08.8628650Z         >>>
2025-03-04T20:59:08.8628808Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-03-04T20:59:08.8629065Z         >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2025-03-04T20:59:08.8629219Z         >>> print(ret)  # prints tensor([4., 4.])
2025-03-04T20:59:08.8629319Z     
2025-03-04T20:59:08.8629582Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8629704Z     
2025-03-04T20:59:08.8629810Z       warnings.warn(msg)
2025-03-04T20:59:08.8629909Z     
2025-03-04T20:59:08.8630127Z --- Parse Warning: 76 / 116 ---
2025-03-04T20:59:08.8631228Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108.
2025-03-04T20:59:08.8631502Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8631604Z     
2025-03-04T20:59:08.8631820Z     Set device mapping between each RPC caller and callee pair. This
2025-03-04T20:59:08.8632027Z     function can be called multiple times to incrementally add
2025-03-04T20:59:08.8632152Z     device placement configurations.
2025-03-04T20:59:08.8632251Z     
2025-03-04T20:59:08.8632342Z     Args:
2025-03-04T20:59:08.8632461Z         to (str): Callee name.
2025-03-04T20:59:08.8632671Z         device_map (Dict of int, str, or torch.device): Device placement
2025-03-04T20:59:08.8632870Z             mappings from this worker to the callee. This map must be
2025-03-04T20:59:08.8632970Z             invertible.
2025-03-04T20:59:08.8633067Z     
2025-03-04T20:59:08.8633219Z     Example:
2025-03-04T20:59:08.8633357Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8633482Z         >>> # both workers
2025-03-04T20:59:08.8633595Z         >>> def add(x, y):
2025-03-04T20:59:08.8633741Z         >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2025-03-04T20:59:08.8633868Z         >>>     return x + y, (x + y).to(2)
2025-03-04T20:59:08.8633962Z         >>>
2025-03-04T20:59:08.8634072Z         >>> # on worker 0
2025-03-04T20:59:08.8634220Z         >>> options = TensorPipeRpcBackendOptions(
2025-03-04T20:59:08.8634344Z         >>>     num_worker_threads=8,
2025-03-04T20:59:08.8634467Z         >>>     device_maps={"worker1": {0: 1}}
2025-03-04T20:59:08.8634618Z         >>> # maps worker0's cuda:0 to worker1's cuda:1
2025-03-04T20:59:08.8634709Z         >>> )
2025-03-04T20:59:08.8634858Z         >>> options.set_device_map("worker1", {1: 2})
2025-03-04T20:59:08.8634994Z         >>> # maps worker0's cuda:1 to worker1's cuda:2
2025-03-04T20:59:08.8635096Z         >>>
2025-03-04T20:59:08.8635198Z         >>> rpc.init_rpc(
2025-03-04T20:59:08.8635306Z         >>>     "worker0",
2025-03-04T20:59:08.8635402Z         >>>     rank=0,
2025-03-04T20:59:08.8635502Z         >>>     world_size=2,
2025-03-04T20:59:08.8635650Z         >>>     backend=rpc.BackendType.TENSORPIPE,
2025-03-04T20:59:08.8635770Z         >>>     rpc_backend_options=options
2025-03-04T20:59:08.8635896Z         >>> )
2025-03-04T20:59:08.8635985Z         >>>
2025-03-04T20:59:08.8636102Z         >>> x = torch.ones(2)
2025-03-04T20:59:08.8636273Z         >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2025-03-04T20:59:08.8636477Z         >>> # The first argument will be moved to cuda:1 on worker1. When
2025-03-04T20:59:08.8636672Z         >>> # sending the return value back, it will follow the invert of
2025-03-04T20:59:08.8636867Z         >>> # the device map, and hence will be moved back to cuda:0 and
2025-03-04T20:59:08.8636971Z         >>> # cuda:1 on worker0
2025-03-04T20:59:08.8637140Z         >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2025-03-04T20:59:08.8637297Z         >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2025-03-04T20:59:08.8637396Z     
2025-03-04T20:59:08.8637663Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8637762Z     
2025-03-04T20:59:08.8637870Z       warnings.warn(msg)
2025-03-04T20:59:08.8637967Z     
2025-03-04T20:59:08.8638164Z --- Parse Warning: 77 / 116 ---
2025-03-04T20:59:08.8639351Z     /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.
2025-03-04T20:59:08.8639627Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8639755Z     
2025-03-04T20:59:08.8639962Z     It has the same API as ``torch.autograd.profiler.profile`` class,
2025-03-04T20:59:08.8640264Z     except that it enables profiling on all threads running RPC server request callbacks.
2025-03-04T20:59:08.8640356Z     
2025-03-04T20:59:08.8640659Z     Context manager that manages autograd profiler state and holds a summary of results.
2025-03-04T20:59:08.8640903Z     Under the hood it just records events of functions being executed in C++ and
2025-03-04T20:59:08.8641159Z     exposes those events to Python. You can wrap any code into it and it will
2025-03-04T20:59:08.8641298Z     only report runtime of PyTorch functions.
2025-03-04T20:59:08.8641585Z     Note: profiler is thread local and is automatically propagated into the async tasks
2025-03-04T20:59:08.8641673Z     
2025-03-04T20:59:08.8646262Z     Args:
2025-03-04T20:59:08.8646585Z         enabled (bool, optional): Setting this to False makes this context manager a no-op.
2025-03-04T20:59:08.8646711Z             Default: ``True``.
2025-03-04T20:59:08.8646802Z     
2025-03-04T20:59:08.8647114Z         use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
2025-03-04T20:59:08.8647395Z             Adds approximately 4us of overhead to each tensor operation.
2025-03-04T20:59:08.8647514Z             Default: ``False``
2025-03-04T20:59:08.8647603Z     
2025-03-04T20:59:08.8647853Z         record_shapes (bool, optional): If shapes recording is set, information
2025-03-04T20:59:08.8648095Z             about input dimensions will be collected. This allows one to see which
2025-03-04T20:59:08.8648334Z             dimensions have been used under the hood and further group by them
2025-03-04T20:59:08.8648564Z             using prof.key_averages(group_by_input_shape=True). Please note that
2025-03-04T20:59:08.8648816Z             shape recording might skew your profiling data. It is recommended to
2025-03-04T20:59:08.8649062Z             use separate runs with and without shape recording to validate the timing.
2025-03-04T20:59:08.8649311Z             Most likely the skew will be negligible for bottom most events (in a case
2025-03-04T20:59:08.8649540Z             of nested function calls). But for higher level functions the total
2025-03-04T20:59:08.8649769Z             self cpu time might be artificially increased because of the shape
2025-03-04T20:59:08.8649869Z             collection.
2025-03-04T20:59:08.8649970Z     
2025-03-04T20:59:08.8650286Z         profile_memory (bool, optional): Whether to report memory usage, default: ``False``
2025-03-04T20:59:08.8650386Z     
2025-03-04T20:59:08.8650481Z     .. warning:
2025-03-04T20:59:08.8650707Z         Enabling memory profiling incurs additional profiler overhead
2025-03-04T20:59:08.8650800Z     
2025-03-04T20:59:08.8650892Z     .. warning:
2025-03-04T20:59:08.8651166Z         Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
2025-03-04T20:59:08.8651373Z         one cannot use the profiler with ``use_cuda = True`` to benchmark
2025-03-04T20:59:08.8651631Z         DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
2025-03-04T20:59:08.8651805Z         please use ``use_cuda = False`` or ``num_workers = 0``.
2025-03-04T20:59:08.8651907Z     
2025-03-04T20:59:08.8652000Z     Example:
2025-03-04T20:59:08.8652121Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8652221Z         >>> # On worker 0:
2025-03-04T20:59:08.8652331Z         >>> import torch
2025-03-04T20:59:08.8652468Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8652627Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-03-04T20:59:08.8652787Z         >>> x, y = torch.tensor(1), torch.tensor(2)
2025-03-04T20:59:08.8652924Z         >>> outer_profile_rref = rpc.remote(
2025-03-04T20:59:08.8653123Z         ...     dst_worker_name, rpc._server_process_global_profile
2025-03-04T20:59:08.8653228Z         ... )
2025-03-04T20:59:08.8653367Z         >>> outer_profile_rref.rpc_sync().__enter__()
2025-03-04T20:59:08.8653536Z         >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
2025-03-04T20:59:08.8653660Z         >>> inner_profile_rref = rpc.remote(
2025-03-04T20:59:08.8653849Z         ...     dst_worker_name, rpc._server_process_global_profile
2025-03-04T20:59:08.8653939Z         ... )
2025-03-04T20:59:08.8654096Z         >>> inner_profile_rref.rpc_sync().__enter__()
2025-03-04T20:59:08.8654253Z         >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
2025-03-04T20:59:08.8654452Z         >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-04T20:59:08.8654638Z         >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
2025-03-04T20:59:08.8654821Z         >>> print(inner_profile_rref.rpc_sync().key_averages())
2025-03-04T20:59:08.8655071Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:08.8655410Z         Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-04T20:59:08.8655650Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:08.8655888Z         sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
2025-03-04T20:59:08.8656087Z         empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
2025-03-04T20:59:08.8656336Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:08.8656456Z         Self CPU time total: 89.667us
2025-03-04T20:59:08.8656638Z         >>> print(outer_profile_rref.rpc_sync().key_averages())
2025-03-04T20:59:08.8656873Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:08.8657201Z         Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-03-04T20:59:08.8657435Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:08.8657638Z         sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
2025-03-04T20:59:08.8657924Z         empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
2025-03-04T20:59:08.8658162Z         add        51.68%           110.550us        58.09%           124.259us        124.259us        1
2025-03-04T20:59:08.8658401Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-03-04T20:59:08.8658532Z         Self CPU time total: 213.926us
2025-03-04T20:59:08.8658640Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8658740Z     
2025-03-04T20:59:08.8658840Z         >>> # On worker 1:
2025-03-04T20:59:08.8658990Z         >>> import torch.distributed.rpc as rpc
2025-03-04T20:59:08.8659141Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-03-04T20:59:08.8659325Z         >>> # wait for worker 0 to finish work, and then shutdown.
2025-03-04T20:59:08.8659432Z         >>> rpc.shutdown()
2025-03-04T20:59:08.8659535Z     
2025-03-04T20:59:08.8659804Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8659907Z     
2025-03-04T20:59:08.8660015Z       warnings.warn(msg)
2025-03-04T20:59:08.8660115Z     
2025-03-04T20:59:08.8660361Z --- Parse Warning: 78 / 116 ---
2025-03-04T20:59:08.8661456Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=33.
2025-03-04T20:59:08.8661736Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8661836Z     
2025-03-04T20:59:08.8662110Z     :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2025-03-04T20:59:08.8662412Z     to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2025-03-04T20:59:08.8662689Z     the local components of :class:`DTensor`, call the function, and wrap the outputs to
2025-03-04T20:59:08.8662870Z     :class:`DTensor` according to the ``out_placements``.
2025-03-04T20:59:08.8662962Z     
2025-03-04T20:59:08.8663069Z     Args:
2025-03-04T20:59:08.8663290Z         func (Callable): the function to be applied on each local shard of
2025-03-04T20:59:08.8663413Z             :class:`DTensor` s.
2025-03-04T20:59:08.8663650Z         out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2025-03-04T20:59:08.8663930Z             the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2025-03-04T20:59:08.8664181Z             If the flattened ``output`` is a single value, the ``out_placements`` should be
2025-03-04T20:59:08.8664447Z             of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2025-03-04T20:59:08.8664729Z             values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2025-03-04T20:59:08.8664874Z             mapping to the flattened ``output``.
2025-03-04T20:59:08.8665088Z             Besides, for :class:`Tensor` output, we use `PlacementType` as its
2025-03-04T20:59:08.8665384Z             placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2025-03-04T20:59:08.8665488Z             should be `None`.
2025-03-04T20:59:08.8665751Z             Note that the only exception is when no :class:`DTensor` argument is passed
2025-03-04T20:59:08.8665983Z             in. In this case, even if `out_placements` is not `None`, the result function
2025-03-04T20:59:08.8666262Z             should ignore the desired placements because the function is not running with
2025-03-04T20:59:08.8666367Z             :class:`DTensor` s.
2025-03-04T20:59:08.8666550Z         in_placements (Tuple[`PlacementType`, ...], optional):
2025-03-04T20:59:08.8666838Z             the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2025-03-04T20:59:08.8667094Z             If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2025-03-04T20:59:08.8667330Z             placements of each :class:`DTensor` argument is the same as the required
2025-03-04T20:59:08.8667563Z             placements or not. If the placements are not the same and
2025-03-04T20:59:08.8667815Z             ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2025-03-04T20:59:08.8668082Z             ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2025-03-04T20:59:08.8668352Z             the required sharding placements before passing its local tensor to ``func``.
2025-03-04T20:59:08.8668601Z             The only exception is when required placements are not ``None`` and the
2025-03-04T20:59:08.8668852Z             argument is a :class:`torch.Tensor`. In this case, the placements examination
2025-03-04T20:59:08.8669090Z             will be skipped and the argument will be directly passed to ``func``.
2025-03-04T20:59:08.8669322Z             If ``in_placements`` is ``None``, no placements examination will be performed.
2025-03-04T20:59:08.8669435Z             Default: None
2025-03-04T20:59:08.8669582Z         device_mesh (:class:`DeviceMesh`, optional):
2025-03-04T20:59:08.8669817Z             the device mesh that all the :class:`DTensor` s are placed on. If not
2025-03-04T20:59:08.8670091Z             specified, this will be inferred from the input :class:`DTensor` s' device
2025-03-04T20:59:08.8670347Z             mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
2025-03-04T20:59:08.8670488Z             device mesh. Default: None.
2025-03-04T20:59:08.8670631Z         redistribute_inputs (bool, optional):
2025-03-04T20:59:08.8670893Z             the bool value indicating whether to reshard the input :class:`DTensor` s when
2025-03-04T20:59:08.8671159Z             their placements are different from the required input placements. If this
2025-03-04T20:59:08.8671397Z             value is ``False`` and some :class:`DTensor` input has a different placement,
2025-03-04T20:59:08.8671554Z             an exception will be raised. Default: False.
2025-03-04T20:59:08.8671642Z     
2025-03-04T20:59:08.8671751Z     Returns:
2025-03-04T20:59:08.8672018Z         A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2025-03-04T20:59:08.8672279Z         and returns a :class:`DTensor` constructed from the return value of ``func``.
2025-03-04T20:59:08.8672366Z     
2025-03-04T20:59:08.8672469Z     Raises:
2025-03-04T20:59:08.8672730Z         AssertionError: If the input :class:`DTensor` is not placed on the same device
2025-03-04T20:59:08.8672988Z             mesh, or if they are placed on a different device mesh than the ``device_mesh``
2025-03-04T20:59:08.8673097Z             argument passed in.
2025-03-04T20:59:08.8673197Z     
2025-03-04T20:59:08.8673449Z         AssertionError: For any non-DTensor output, we require its corresponding
2025-03-04T20:59:08.8674020Z             output placement in ``out_placements`` be None. An AssertionError will be raised
2025-03-04T20:59:08.8674138Z             if this is not the case.
2025-03-04T20:59:08.8674242Z     
2025-03-04T20:59:08.8674520Z         ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2025-03-04T20:59:08.8674699Z             a redistribution according to ``in_placements``.
2025-03-04T20:59:08.8674788Z     
2025-03-04T20:59:08.8674896Z     Example:
2025-03-04T20:59:08.8675021Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8675183Z         >>> def mm_allreduce_forward(device_mesh, W, X):
2025-03-04T20:59:08.8675315Z         >>>     partial_sum_tensor = torch.mm(W, X)
2025-03-04T20:59:08.8675581Z         >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2025-03-04T20:59:08.8675695Z         >>>     return reduced_tensor
2025-03-04T20:59:08.8675799Z         >>>
2025-03-04T20:59:08.8675941Z         >>> W = torch.randn(12, 8, requires_grad=False)
2025-03-04T20:59:08.8676090Z         >>> X = torch.randn(8, 16, requires_grad=False)
2025-03-04T20:59:08.8676194Z         >>> Y = torch.mm(W, X)
2025-03-04T20:59:08.8676407Z         >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2025-03-04T20:59:08.8676642Z         >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2025-03-04T20:59:08.8676747Z         >>>
2025-03-04T20:59:08.8677030Z         >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
2025-03-04T20:59:08.8677178Z         >>> local_mm_allreduce_forward = local_map(
2025-03-04T20:59:08.8677291Z         >>>     mm_allreduce_forward,
2025-03-04T20:59:08.8677427Z         >>>     out_placements=[Replicate()],
2025-03-04T20:59:08.8677558Z         >>>     in_placements=[col_wise, row_wise],
2025-03-04T20:59:08.8677686Z         >>>     device_mesh=device_mesh,
2025-03-04T20:59:08.8677776Z         >>> )
2025-03-04T20:59:08.8677878Z         >>>
2025-03-04T20:59:08.8677993Z         >>> W_dt = distribute_tensor(
2025-03-04T20:59:08.8678108Z         ...     W, device_mesh, (col_wise)
2025-03-04T20:59:08.8678243Z         ... )  # col-wisely sharded W tensor
2025-03-04T20:59:08.8678356Z         >>> X_dt = distribute_tensor(
2025-03-04T20:59:08.8678482Z         ...     X, device_mesh, (row_wise)
2025-03-04T20:59:08.8678603Z         ... )  # row-wisely sharded X tensor
2025-03-04T20:59:08.8678779Z         >>> Y_dt = local_mm_allreduce_forward(
2025-03-04T20:59:08.8678890Z         ...     device_mesh, W_dt, X_dt
2025-03-04T20:59:08.8679091Z         ... )  # apply local_mm_allreduce_forward to DTensors
2025-03-04T20:59:08.8679183Z     
2025-03-04T20:59:08.8679413Z     .. note:: This API is currently experimental and subject to change
2025-03-04T20:59:08.8679500Z     
2025-03-04T20:59:08.8679776Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8679865Z     
2025-03-04T20:59:08.8679984Z       warnings.warn(msg)
2025-03-04T20:59:08.8680074Z     
2025-03-04T20:59:08.8680312Z --- Parse Warning: 79 / 116 ---
2025-03-04T20:59:08.8681410Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=26.
2025-03-04T20:59:08.8681703Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8681792Z     
2025-03-04T20:59:08.8682098Z     :meth:`register_sharding` is an experimental API that allows users to register sharding
2025-03-04T20:59:08.8682351Z     strategies for an operator when the tensor inputs and outputs are DTensor.
2025-03-04T20:59:08.8682627Z     It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2025-03-04T20:59:08.8682879Z     e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2025-03-04T20:59:08.8683203Z     when users would like to overwrite default sharding strategies of existing operators.
2025-03-04T20:59:08.8683295Z     
2025-03-04T20:59:08.8683396Z     Args:
2025-03-04T20:59:08.8683534Z         op (Union[OpOverload, List[OpOverload]]):
2025-03-04T20:59:08.8683753Z             An op or a list of ops to register the customized sharding function.
2025-03-04T20:59:08.8683846Z     
2025-03-04T20:59:08.8683951Z     Returns:
2025-03-04T20:59:08.8684228Z         A function decorator which can be used to wrap a function that defines the sharding
2025-03-04T20:59:08.8684527Z         strategy for the operator specified in ``op``. The defined sharding strategy will be
2025-03-04T20:59:08.8684813Z         registered to DTensor and will override the default sharding strategy if DTensor has
2025-03-04T20:59:08.8685144Z         already implemented the operator. The customized sharding function takes the same inputs
2025-03-04T20:59:08.8685396Z         as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2025-03-04T20:59:08.8685687Z         replaced by a tensor-like object that DTensor uses internally). The function should
2025-03-04T20:59:08.8685967Z         return a sequence of 2-tuples, each specifying acceptable output placements and its
2025-03-04T20:59:08.8686131Z         corresponding intput placements.
2025-03-04T20:59:08.8686218Z     
2025-03-04T20:59:08.8686324Z     Example:
2025-03-04T20:59:08.8686455Z         >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8686610Z         >>> @register_sharding(aten._softmax.default)
2025-03-04T20:59:08.8686777Z         >>> def custom_softmax_sharding(x, dim, half_to_float):
2025-03-04T20:59:08.8686941Z         >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2025-03-04T20:59:08.8687062Z         >>>     acceptable_shardings = []
2025-03-04T20:59:08.8687164Z         >>>
2025-03-04T20:59:08.8687351Z         >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2025-03-04T20:59:08.8687518Z         >>>     acceptable_shardings.append(all_replicate)
2025-03-04T20:59:08.8687609Z         >>>
2025-03-04T20:59:08.8687748Z         >>>     for sharding_dim in range(x.ndim):
2025-03-04T20:59:08.8687872Z         >>>         if sharding_dim != softmax_dim:
2025-03-04T20:59:08.8687994Z         >>>             all_sharded = (
2025-03-04T20:59:08.8688114Z         >>>                 [Shard(sharding_dim)],
2025-03-04T20:59:08.8688287Z         >>>                 [Shard(sharding_dim), None, None],
2025-03-04T20:59:08.8688384Z         >>>             )
2025-03-04T20:59:08.8688546Z         >>>             acceptable_shardings.append(all_sharded)
2025-03-04T20:59:08.8688660Z         >>>
2025-03-04T20:59:08.8688795Z         >>>     return acceptable_shardings
2025-03-04T20:59:08.8688884Z     
2025-03-04T20:59:08.8689103Z     .. note:: This API is currently experimental and subject to change
2025-03-04T20:59:08.8689190Z     
2025-03-04T20:59:08.8689467Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8689561Z     
2025-03-04T20:59:08.8689679Z       warnings.warn(msg)
2025-03-04T20:59:08.8689767Z     
2025-03-04T20:59:08.8689967Z --- Parse Warning: 80 / 116 ---
2025-03-04T20:59:08.8691013Z     /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=403.
2025-03-04T20:59:08.8691302Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8691390Z     
2025-03-04T20:59:08.8691798Z     Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2025-03-04T20:59:08.8692130Z     ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2025-03-04T20:59:08.8692231Z     
2025-03-04T20:59:08.8692329Z     Keyword Args:
2025-03-04T20:59:08.8692581Z         input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-04T20:59:08.8692924Z             The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-03-04T20:59:08.8693303Z             DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-03-04T20:59:08.8693434Z             as a placeholder. default: None.
2025-03-04T20:59:08.8693687Z         desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-03-04T20:59:08.8694079Z             The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-04T20:59:08.8694494Z             have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-03-04T20:59:08.8694637Z         input_kwarg_layouts (Dict[str, Placement]):
2025-03-04T20:59:08.8695038Z             The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-03-04T20:59:08.8695143Z             default: None
2025-03-04T20:59:08.8695328Z         desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-03-04T20:59:08.8695708Z             The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-03-04T20:59:08.8695907Z             have the desired DTensor layouts. default: None.
2025-03-04T20:59:08.8696033Z         use_local_output (bool, optional):
2025-03-04T20:59:08.8696419Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-03-04T20:59:08.8696515Z     Returns:
2025-03-04T20:59:08.8696851Z         A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2025-03-04T20:59:08.8696940Z     
2025-03-04T20:59:08.8697049Z     Example::
2025-03-04T20:59:08.8697166Z         >>> # xdoctest: +SKIP(failing)
2025-03-04T20:59:08.8697501Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2025-03-04T20:59:08.8697702Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-04T20:59:08.8697879Z         >>> ...
2025-03-04T20:59:08.8698201Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-04T20:59:08.8698401Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-04T20:59:08.8698492Z         >>>
2025-03-04T20:59:08.8698881Z         >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2025-03-04T20:59:08.8699042Z         >>> # and then redistributed to Replicated DTensor.
2025-03-04T20:59:08.8699163Z         >>> parallelize_module(
2025-03-04T20:59:08.8699307Z         >>>     block, # this can be a submodule or module
2025-03-04T20:59:08.8699418Z         >>>     tp_mesh,
2025-03-04T20:59:08.8699530Z         >>>     parallelize_plan={
2025-03-04T20:59:08.8699662Z         >>>         "attn": PrepareModuleInput(
2025-03-04T20:59:08.8699820Z         >>>             input_layouts=(Shard(0), None, None, ...),
2025-03-04T20:59:08.8699995Z         >>>             desired_input_layouts=(Replicate(), None, None, ...)
2025-03-04T20:59:08.8700101Z         >>>         ),
2025-03-04T20:59:08.8700193Z         >>>     }
2025-03-04T20:59:08.8700297Z         >>> )
2025-03-04T20:59:08.8700385Z     
2025-03-04T20:59:08.8700710Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8700798Z     
2025-03-04T20:59:08.8700915Z       warnings.warn(msg)
2025-03-04T20:59:08.8701003Z     
2025-03-04T20:59:08.8701224Z --- Parse Warning: 81 / 116 ---
2025-03-04T20:59:08.8702268Z     /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=562.
2025-03-04T20:59:08.8702580Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8702668Z     
2025-03-04T20:59:08.8703084Z     Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2025-03-04T20:59:08.8703429Z     ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2025-03-04T20:59:08.8703531Z     
2025-03-04T20:59:08.8703626Z     Keyword Args:
2025-03-04T20:59:08.8703812Z         output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-04T20:59:08.8704162Z             The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-03-04T20:59:08.8704558Z             DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-03-04T20:59:08.8704710Z             ``None`` need to be specified as a placeholder.
2025-03-04T20:59:08.8704931Z         desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-03-04T20:59:08.8705329Z             The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-03-04T20:59:08.8705492Z             have the desired DTensor layouts.
2025-03-04T20:59:08.8705614Z         use_local_output (bool, optional):
2025-03-04T20:59:08.8706001Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-03-04T20:59:08.8706094Z     Returns:
2025-03-04T20:59:08.8706414Z         A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2025-03-04T20:59:08.8706502Z     
2025-03-04T20:59:08.8706612Z     Example::
2025-03-04T20:59:08.8706727Z         >>> # xdoctest: +SKIP(failing)
2025-03-04T20:59:08.8707063Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2025-03-04T20:59:08.8707266Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-03-04T20:59:08.8707370Z         >>> ...
2025-03-04T20:59:08.8707686Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-03-04T20:59:08.8707840Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-03-04T20:59:08.8707933Z         >>>
2025-03-04T20:59:08.8708357Z         >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2025-03-04T20:59:08.8708529Z         >>> # and then redistributed to Sharded DTensor.
2025-03-04T20:59:08.8708673Z         >>> parallelize_module(
2025-03-04T20:59:08.8708819Z         >>>     block, # this can be a submodule or module
2025-03-04T20:59:08.8708931Z         >>>     tp_mesh,
2025-03-04T20:59:08.8709079Z         >>>     parallelize_plan = PrepareModuleOutput(
2025-03-04T20:59:08.8709218Z         >>>         output_layouts=Replicate(),
2025-03-04T20:59:08.8709345Z         >>>         desired_output_layouts=Shard(0)
2025-03-04T20:59:08.8709447Z         >>>     )
2025-03-04T20:59:08.8709538Z         >>> )
2025-03-04T20:59:08.8709632Z     
2025-03-04T20:59:08.8709897Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8709999Z     
2025-03-04T20:59:08.8710103Z       warnings.warn(msg)
2025-03-04T20:59:08.8710203Z     
2025-03-04T20:59:08.8710403Z --- Parse Warning: 82 / 116 ---
2025-03-04T20:59:08.8711512Z     /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=55.
2025-03-04T20:59:08.8711785Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8711883Z     
2025-03-04T20:59:08.8712199Z     Creates a multivariate normal distribution with covariance matrix having a low-rank form
2025-03-04T20:59:08.8712433Z     parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
2025-03-04T20:59:08.8712524Z     
2025-03-04T20:59:08.8712723Z         covariance_matrix = cov_factor @ cov_factor.T + cov_diag
2025-03-04T20:59:08.8712812Z     
2025-03-04T20:59:08.8712919Z     Example:
2025-03-04T20:59:08.8713074Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-03-04T20:59:08.8713237Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:08.8713367Z         >>> m = LowRankMultivariateNormal(
2025-03-04T20:59:08.8713568Z         ...     torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2)
2025-03-04T20:59:08.8713663Z         ... )
2025-03-04T20:59:08.8713975Z         >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
2025-03-04T20:59:08.8714080Z         tensor([-0.2102, -0.5429])
2025-03-04T20:59:08.8714180Z     
2025-03-04T20:59:08.8714268Z     Args:
2025-03-04T20:59:08.8714525Z         loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
2025-03-04T20:59:08.8714786Z         cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
2025-03-04T20:59:08.8714930Z             `batch_shape + event_shape + (rank,)`
2025-03-04T20:59:08.8715218Z         cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
2025-03-04T20:59:08.8715345Z             `batch_shape + event_shape`
2025-03-04T20:59:08.8715433Z     
2025-03-04T20:59:08.8715531Z     Note:
2025-03-04T20:59:08.8715805Z         The computation for determinant and inverse of covariance matrix is avoided when
2025-03-04T20:59:08.8716067Z         `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
2025-03-04T20:59:08.8716282Z         <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
2025-03-04T20:59:08.8716596Z         `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
2025-03-04T20:59:08.8716856Z         Thanks to these formulas, we just need to compute the determinant and inverse of
2025-03-04T20:59:08.8716999Z         the small size "capacitance" matrix::
2025-03-04T20:59:08.8717085Z     
2025-03-04T20:59:08.8717289Z             capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
2025-03-04T20:59:08.8717377Z     
2025-03-04T20:59:08.8717638Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8717768Z     
2025-03-04T20:59:08.8717872Z       warnings.warn(msg)
2025-03-04T20:59:08.8717975Z     
2025-03-04T20:59:08.8718173Z --- Parse Warning: 83 / 116 ---
2025-03-04T20:59:08.8719229Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13.
2025-03-04T20:59:08.8719498Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8719601Z     
2025-03-04T20:59:08.8719839Z     The `MixtureSameFamily` distribution implements a (batch of) mixture
2025-03-04T20:59:08.8720105Z     distribution where all component are from different parameterizations of
2025-03-04T20:59:08.8720326Z     the same distribution type. It is parameterized by a `Categorical`
2025-03-04T20:59:08.8720548Z     "selecting distribution" (over `k` component) and a component
2025-03-04T20:59:08.8720762Z     distribution, i.e., a `Distribution` with a rightmost batch shape
2025-03-04T20:59:08.8720940Z     (equal to `[k]`) which indexes each (batch of) component.
2025-03-04T20:59:08.8721025Z     
2025-03-04T20:59:08.8721133Z     Examples::
2025-03-04T20:59:08.8721217Z     
2025-03-04T20:59:08.8721356Z         >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.8721563Z         >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2025-03-04T20:59:08.8721696Z         >>> # weighted normal distributions
2025-03-04T20:59:08.8721818Z         >>> mix = D.Categorical(torch.ones(5,))
2025-03-04T20:59:08.8722009Z         >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2025-03-04T20:59:08.8722136Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-03-04T20:59:08.8722235Z     
2025-03-04T20:59:08.8722445Z         >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2025-03-04T20:59:08.8722597Z         >>> # weighted bivariate normal distributions
2025-03-04T20:59:08.8722723Z         >>> mix = D.Categorical(torch.ones(5,))
2025-03-04T20:59:08.8722857Z         >>> comp = D.Independent(D.Normal(
2025-03-04T20:59:08.8722992Z         ...          torch.randn(5,2), torch.rand(5,2)), 1)
2025-03-04T20:59:08.8723130Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-03-04T20:59:08.8723215Z     
2025-03-04T20:59:08.8723417Z         >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2025-03-04T20:59:08.8723626Z         >>> # consisting of 5 random weighted bivariate normal distributions
2025-03-04T20:59:08.8723767Z         >>> mix = D.Categorical(torch.rand(3,5))
2025-03-04T20:59:08.8723884Z         >>> comp = D.Independent(D.Normal(
2025-03-04T20:59:08.8724038Z         ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2025-03-04T20:59:08.8724165Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-03-04T20:59:08.8724291Z     
2025-03-04T20:59:08.8724378Z     Args:
2025-03-04T20:59:08.8724603Z         mixture_distribution: `torch.distributions.Categorical`-like
2025-03-04T20:59:08.8724797Z             instance. Manages the probability of selecting component.
2025-03-04T20:59:08.8724991Z             The number of categories must match the rightmost batch
2025-03-04T20:59:08.8725185Z             dimension of the `component_distribution`. Must have either
2025-03-04T20:59:08.8725345Z             scalar `batch_shape` or `batch_shape` matching
2025-03-04T20:59:08.8725486Z             `component_distribution.batch_shape[:-1]`
2025-03-04T20:59:08.8725725Z         component_distribution: `torch.distributions.Distribution`-like
2025-03-04T20:59:08.8725908Z             instance. Right-most batch dimension indexes component.
2025-03-04T20:59:08.8726007Z     
2025-03-04T20:59:08.8726265Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8726362Z     
2025-03-04T20:59:08.8726469Z       warnings.warn(msg)
2025-03-04T20:59:08.8726567Z     
2025-03-04T20:59:08.8726759Z --- Parse Warning: 84 / 116 ---
2025-03-04T20:59:08.8727813Z     /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=111.
2025-03-04T20:59:08.8728085Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8728179Z     
2025-03-04T20:59:08.8728369Z     Creates a RelaxedBernoulli distribution, parametrized by
2025-03-04T20:59:08.8728577Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2025-03-04T20:59:08.8728800Z     (but not both). This is a relaxed version of the `Bernoulli` distribution,
2025-03-04T20:59:08.8729002Z     so the values are in (0, 1), and has reparametrizable samples.
2025-03-04T20:59:08.8729088Z     
2025-03-04T20:59:08.8729194Z     Example::
2025-03-04T20:59:08.8729280Z     
2025-03-04T20:59:08.8729426Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:08.8729580Z         >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2025-03-04T20:59:08.8729711Z         ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2025-03-04T20:59:08.8729820Z         >>> m.sample()
2025-03-04T20:59:08.8729948Z         tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2025-03-04T20:59:08.8730047Z     
2025-03-04T20:59:08.8730136Z     Args:
2025-03-04T20:59:08.8730295Z         temperature (Tensor): relaxation temperature
2025-03-04T20:59:08.8730471Z         probs (Number, Tensor): the probability of sampling `1`
2025-03-04T20:59:08.8730674Z         logits (Number, Tensor): the log-odds of sampling `1`
2025-03-04T20:59:08.8730759Z     
2025-03-04T20:59:08.8731033Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8731117Z     
2025-03-04T20:59:08.8731234Z       warnings.warn(msg)
2025-03-04T20:59:08.8731321Z     
2025-03-04T20:59:08.8731525Z --- Parse Warning: 85 / 116 ---
2025-03-04T20:59:08.8732583Z     /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=101.
2025-03-04T20:59:08.8732864Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8732946Z     
2025-03-04T20:59:08.8733180Z     Creates a RelaxedOneHotCategorical distribution parametrized by
2025-03-04T20:59:08.8733380Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2025-03-04T20:59:08.8733640Z     This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2025-03-04T20:59:08.8733808Z     its samples are on simplex, and are reparametrizable.
2025-03-04T20:59:08.8733904Z     
2025-03-04T20:59:08.8733995Z     Example::
2025-03-04T20:59:08.8734133Z     
2025-03-04T20:59:08.8734278Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:08.8734456Z         >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2025-03-04T20:59:08.8734587Z         ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2025-03-04T20:59:08.8734695Z         >>> m.sample()
2025-03-04T20:59:08.8734818Z         tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2025-03-04T20:59:08.8734914Z     
2025-03-04T20:59:08.8735001Z     Args:
2025-03-04T20:59:08.8735163Z         temperature (Tensor): relaxation temperature
2025-03-04T20:59:08.8735284Z         probs (Tensor): event probabilities
2025-03-04T20:59:08.8735491Z         logits (Tensor): unnormalized log probability for each event
2025-03-04T20:59:08.8735579Z     
2025-03-04T20:59:08.8735849Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8735933Z     
2025-03-04T20:59:08.8736047Z       warnings.warn(msg)
2025-03-04T20:59:08.8736134Z     
2025-03-04T20:59:08.8736335Z --- Parse Warning: 86 / 116 ---
2025-03-04T20:59:08.8737352Z     /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.
2025-03-04T20:59:08.8737687Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8737967Z     Return a new dict with new, potentially nested, key value pair
2025-03-04T20:59:08.8738065Z     
2025-03-04T20:59:08.8738166Z         >>> purchase = {
2025-03-04T20:59:08.8738276Z         ...     "name": "Alice",
2025-03-04T20:59:08.8738461Z         ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-04T20:59:08.8738594Z         ...     "credit card": "5555-1234-1234-1234",
2025-03-04T20:59:08.8738684Z         ... }
2025-03-04T20:59:08.8738896Z         >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2025-03-04T20:59:08.8739026Z         {'credit card': '5555-1234-1234-1234',
2025-03-04T20:59:08.8739127Z          'name': 'Alice',
2025-03-04T20:59:08.8739310Z          'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2025-03-04T20:59:08.8739396Z         
2025-03-04T20:59:08.8739668Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8739752Z     
2025-03-04T20:59:08.8739868Z       warnings.warn(msg)
2025-03-04T20:59:08.8739951Z     
2025-03-04T20:59:08.8740159Z --- Parse Warning: 87 / 116 ---
2025-03-04T20:59:08.8741209Z     /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.
2025-03-04T20:59:08.8741492Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8741652Z     Update value in a (potentially) nested dictionary
2025-03-04T20:59:08.8741750Z     
2025-03-04T20:59:08.8741842Z         inputs:
2025-03-04T20:59:08.8741974Z         d - dictionary on which to operate
2025-03-04T20:59:08.8742201Z         keys - list or tuple giving the location of the value to be changed in d
2025-03-04T20:59:08.8742351Z         func - function to operate on that value
2025-03-04T20:59:08.8742435Z     
2025-03-04T20:59:08.8742645Z         If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2025-03-04T20:59:08.8742885Z         original dictionary with v replaced by func(v), but does not mutate the
2025-03-04T20:59:08.8743003Z         original dictionary.
2025-03-04T20:59:08.8743084Z     
2025-03-04T20:59:08.8743310Z         If k0 is not a key in d, update_in creates nested dictionaries to the depth
2025-03-04T20:59:08.8743529Z         specified by the keys, with the innermost value set to func(default).
2025-03-04T20:59:08.8743647Z     
2025-03-04T20:59:08.8743749Z         >>> inc = lambda x: x + 1
2025-03-04T20:59:08.8743872Z         >>> update_in({"a": 0}, ["a"], inc)
2025-03-04T20:59:08.8743962Z         {'a': 1}
2025-03-04T20:59:08.8744055Z     
2025-03-04T20:59:08.8744155Z         >>> transaction = {
2025-03-04T20:59:08.8744266Z         ...     "name": "Alice",
2025-03-04T20:59:08.8744465Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-04T20:59:08.8744604Z         ...     "credit card": "5555-1234-1234-1234",
2025-03-04T20:59:08.8744691Z         ... }
2025-03-04T20:59:08.8744924Z         >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2025-03-04T20:59:08.8745037Z         {'credit card': '5555-1234-1234-1234',
2025-03-04T20:59:08.8745148Z          'name': 'Alice',
2025-03-04T20:59:08.8745321Z          'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2025-03-04T20:59:08.8745422Z     
2025-03-04T20:59:08.8745547Z         >>> # updating a value when k0 is not in d
2025-03-04T20:59:08.8745696Z         >>> update_in({}, [1, 2, 3], str, default="bar")
2025-03-04T20:59:08.8745789Z         {1: {2: {3: 'bar'}}}
2025-03-04T20:59:08.8745949Z         >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2025-03-04T20:59:08.8746050Z         {1: 'foo', 2: {3: {4: 1}}}
2025-03-04T20:59:08.8746150Z         
2025-03-04T20:59:08.8746432Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8746520Z     
2025-03-04T20:59:08.8746639Z       warnings.warn(msg)
2025-03-04T20:59:08.8746727Z     
2025-03-04T20:59:08.8746932Z --- Parse Warning: 88 / 116 ---
2025-03-04T20:59:08.8747947Z     /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.
2025-03-04T20:59:08.8748232Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8748408Z     Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2025-03-04T20:59:08.8748511Z     
2025-03-04T20:59:08.8748707Z         If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2025-03-04T20:59:08.8748929Z         ``no_default`` is specified, then it raises KeyError or IndexError.
2025-03-04T20:59:08.8749016Z     
2025-03-04T20:59:08.8749243Z         ``get_in`` is a generalization of ``operator.getitem`` for nested data
2025-03-04T20:59:08.8749385Z         structures such as dictionaries and lists.
2025-03-04T20:59:08.8749486Z     
2025-03-04T20:59:08.8749588Z         >>> transaction = {
2025-03-04T20:59:08.8749700Z         ...     "name": "Alice",
2025-03-04T20:59:08.8749921Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-03-04T20:59:08.8750056Z         ...     "credit card": "5555-1234-1234-1234",
2025-03-04T20:59:08.8750147Z         ... }
2025-03-04T20:59:08.8750303Z         >>> get_in(["purchase", "items", 0], transaction)
2025-03-04T20:59:08.8750395Z         'Apple'
2025-03-04T20:59:08.8750520Z         >>> get_in(["name"], transaction)
2025-03-04T20:59:08.8750614Z         'Alice'
2025-03-04T20:59:08.8750761Z         >>> get_in(["purchase", "total"], transaction)
2025-03-04T20:59:08.8750918Z         >>> get_in(["purchase", "items", "apple"], transaction)
2025-03-04T20:59:08.8751074Z         >>> get_in(["purchase", "items", 10], transaction)
2025-03-04T20:59:08.8751221Z         >>> get_in(["purchase", "total"], transaction, 0)
2025-03-04T20:59:08.8751320Z         0
2025-03-04T20:59:08.8751433Z         >>> get_in(["y"], {}, no_default=True)
2025-03-04T20:59:08.8751569Z         Traceback (most recent call last):
2025-03-04T20:59:08.8751656Z             ...
2025-03-04T20:59:08.8751768Z         KeyError: 'y'
2025-03-04T20:59:08.8751851Z     
2025-03-04T20:59:08.8751958Z         See Also:
2025-03-04T20:59:08.8752057Z             itertoolz.get
2025-03-04T20:59:08.8752177Z             operator.getitem
2025-03-04T20:59:08.8752289Z         
2025-03-04T20:59:08.8752563Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8752650Z     
2025-03-04T20:59:08.8752753Z       warnings.warn(msg)
2025-03-04T20:59:08.8752848Z     
2025-03-04T20:59:08.8753040Z --- Parse Warning: 89 / 116 ---
2025-03-04T20:59:08.8754228Z     /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.
2025-03-04T20:59:08.8754516Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8754637Z     Group a collection by a key function
2025-03-04T20:59:08.8754734Z     
2025-03-04T20:59:08.8754903Z         >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2025-03-04T20:59:08.8755031Z         >>> groupby(len, names)  # doctest: +SKIP
2025-03-04T20:59:08.8755211Z         {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2025-03-04T20:59:08.8755297Z     
2025-03-04T20:59:08.8755461Z         >>> iseven = lambda x: x % 2 == 0
2025-03-04T20:59:08.8755637Z         >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2025-03-04T20:59:08.8755762Z         {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2025-03-04T20:59:08.8755876Z     
2025-03-04T20:59:08.8756039Z         Non-callable keys imply grouping on a member.
2025-03-04T20:59:08.8756127Z     
2025-03-04T20:59:08.8756230Z         >>> groupby(
2025-03-04T20:59:08.8756327Z         ...     "gender",
2025-03-04T20:59:08.8756425Z         ...     [
2025-03-04T20:59:08.8756554Z         ...         {"name": "Alice", "gender": "F"},
2025-03-04T20:59:08.8756686Z         ...         {"name": "Bob", "gender": "M"},
2025-03-04T20:59:08.8756813Z         ...         {"name": "Charlie", "gender": "M"},
2025-03-04T20:59:08.8756909Z         ...     ],
2025-03-04T20:59:08.8757015Z         ... )  # doctest:+SKIP
2025-03-04T20:59:08.8757144Z         {'F': [{'gender': 'F', 'name': 'Alice'}],
2025-03-04T20:59:08.8757259Z          'M': [{'gender': 'M', 'name': 'Bob'},
2025-03-04T20:59:08.8757388Z                {'gender': 'M', 'name': 'Charlie'}]}
2025-03-04T20:59:08.8757474Z     
2025-03-04T20:59:08.8757632Z         Not to be confused with ``itertools.groupby``
2025-03-04T20:59:08.8757719Z     
2025-03-04T20:59:08.8757819Z         See Also:
2025-03-04T20:59:08.8757907Z             countby
2025-03-04T20:59:08.8758003Z         
2025-03-04T20:59:08.8758265Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8758359Z     
2025-03-04T20:59:08.8758461Z       warnings.warn(msg)
2025-03-04T20:59:08.8758548Z     
2025-03-04T20:59:08.8758786Z --- Parse Warning: 90 / 116 ---
2025-03-04T20:59:08.8759715Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601.
2025-03-04T20:59:08.8759988Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8760189Z     Applies Batch Normalization over a N-Dimensional input.
2025-03-04T20:59:08.8760274Z     
2025-03-04T20:59:08.8760640Z         The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2025-03-04T20:59:08.8760877Z         `Batch Normalization: Accelerating Deep Network Training by Reducing
2025-03-04T20:59:08.8761110Z         Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2025-03-04T20:59:08.8761196Z     
2025-03-04T20:59:08.8761306Z         .. math::
2025-03-04T20:59:08.8761390Z     
2025-03-04T20:59:08.8761617Z             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2025-03-04T20:59:08.8761712Z     
2025-03-04T20:59:08.8761948Z         The mean and standard-deviation are calculated per-dimension over all
2025-03-04T20:59:08.8762227Z         mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2025-03-04T20:59:08.8762480Z         are learnable parameter vectors of size `C` (where `C` is the input size).
2025-03-04T20:59:08.8762673Z         By default, the elements of :math:`\gamma` are sampled from
2025-03-04T20:59:08.8762882Z         :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2025-03-04T20:59:08.8763154Z         The standard-deviation is calculated via the biased estimator, equivalent to
2025-03-04T20:59:08.8763279Z         `torch.var(input, unbiased=False)`.
2025-03-04T20:59:08.8763374Z     
2025-03-04T20:59:08.8763616Z         Also by default, during training this layer keeps running estimates of its
2025-03-04T20:59:08.8763864Z         computed mean and variance, which are then used for normalization during
2025-03-04T20:59:08.8764118Z         evaluation. The running estimates are kept with a default :attr:`momentum`
2025-03-04T20:59:08.8764217Z         of 0.1.
2025-03-04T20:59:08.8764303Z     
2025-03-04T20:59:08.8764546Z         If :attr:`track_running_stats` is set to ``False``, this layer then does not
2025-03-04T20:59:08.8764801Z         keep running estimates, and batch statistics are instead used during
2025-03-04T20:59:08.8764921Z         evaluation time as well.
2025-03-04T20:59:08.8765008Z     
2025-03-04T20:59:08.8765132Z         .. note::
2025-03-04T20:59:08.8765362Z             This :attr:`momentum` argument is different from one used in optimizer
2025-03-04T20:59:08.8765602Z             classes and the conventional notion of momentum. Mathematically, the
2025-03-04T20:59:08.8765745Z             update rule for running statistics here is
2025-03-04T20:59:08.8766026Z             :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2025-03-04T20:59:08.8766240Z             where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2025-03-04T20:59:08.8766358Z             new observed value.
2025-03-04T20:59:08.8766446Z     
2025-03-04T20:59:08.8766762Z         Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2025-03-04T20:59:08.8767024Z         statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2025-03-04T20:59:08.8767223Z         Normalization or Spatio-temporal Batch Normalization.
2025-03-04T20:59:08.8767313Z     
2025-03-04T20:59:08.8767476Z         Currently :class:`SyncBatchNorm` only supports
2025-03-04T20:59:08.8767766Z         :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2025-03-04T20:59:08.8767993Z         :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2025-03-04T20:59:08.8768247Z         :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2025-03-04T20:59:08.8768361Z         Network with DDP.
2025-03-04T20:59:08.8768449Z     
2025-03-04T20:59:08.8768548Z         Args:
2025-03-04T20:59:08.8768720Z             num_features: :math:`C` from an expected input of size
2025-03-04T20:59:08.8768835Z                 :math:`(N, C, +)`
2025-03-04T20:59:08.8769031Z             eps: a value added to the denominator for numerical stability.
2025-03-04T20:59:08.8769145Z                 Default: ``1e-5``
2025-03-04T20:59:08.8769346Z             momentum: the value used for the running_mean and running_var
2025-03-04T20:59:08.8769574Z                 computation. Can be set to ``None`` for cumulative moving average
2025-03-04T20:59:08.8769699Z                 (i.e. simple average). Default: 0.1
2025-03-04T20:59:08.8769921Z             affine: a boolean value that when set to ``True``, this module has
2025-03-04T20:59:08.8770080Z                 learnable affine parameters. Default: ``True``
2025-03-04T20:59:08.8770311Z             track_running_stats: a boolean value that when set to ``True``, this
2025-03-04T20:59:08.8770550Z                 module tracks the running mean and variance, and when set to ``False``,
2025-03-04T20:59:08.8770900Z                 this module does not track such statistics, and initializes statistics
2025-03-04T20:59:08.8771112Z                 buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2025-03-04T20:59:08.8771359Z                 When these buffers are ``None``, this module always uses batch statistics.
2025-03-04T20:59:08.8771521Z                 in both training and eval modes. Default: ``True``
2025-03-04T20:59:08.8771789Z             process_group: synchronization of stats happen within each process group
2025-03-04T20:59:08.8772022Z                 individually. Default behavior is synchronization across the whole
2025-03-04T20:59:08.8772124Z                 world
2025-03-04T20:59:08.8772209Z     
2025-03-04T20:59:08.8772314Z         Shape:
2025-03-04T20:59:08.8772428Z             - Input: :math:`(N, C, +)`
2025-03-04T20:59:08.8772596Z             - Output: :math:`(N, C, +)` (same shape as input)
2025-03-04T20:59:08.8772685Z     
2025-03-04T20:59:08.8772783Z         .. note::
2025-03-04T20:59:08.8773032Z             Synchronization of batchnorm statistics occurs only while training, i.e.
2025-03-04T20:59:08.8773268Z             synchronization is disabled when ``model.eval()`` is set or if
2025-03-04T20:59:08.8773400Z             ``self.training`` is otherwise ``False``.
2025-03-04T20:59:08.8773489Z     
2025-03-04T20:59:08.8773779Z         Examples::
2025-03-04T20:59:08.8773980Z     
2025-03-04T20:59:08.8774094Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8774226Z             >>> # With Learnable Parameters
2025-03-04T20:59:08.8774343Z             >>> m = nn.SyncBatchNorm(100)
2025-03-04T20:59:08.8774490Z             >>> # creating process group (optional)
2025-03-04T20:59:08.8774647Z             >>> # ranks is a list of int identifying rank ids.
2025-03-04T20:59:08.8774775Z             >>> ranks = list(range(8))
2025-03-04T20:59:08.8774889Z             >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-04T20:59:08.8775047Z             >>> # Note: every rank calls into new_group for every
2025-03-04T20:59:08.8775224Z             >>> # process group created, even if that rank is not
2025-03-04T20:59:08.8775336Z             >>> # part of the group.
2025-03-04T20:59:08.8775610Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-04T20:59:08.8775826Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-04T20:59:08.8775966Z             >>> # Without Learnable Parameters
2025-03-04T20:59:08.8776184Z             >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2025-03-04T20:59:08.8776335Z             >>> input = torch.randn(20, 100, 35, 45, 10)
2025-03-04T20:59:08.8776444Z             >>> output = m(input)
2025-03-04T20:59:08.8776548Z     
2025-03-04T20:59:08.8776719Z             >>> # network is nn.BatchNorm layer
2025-03-04T20:59:08.8777024Z             >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2025-03-04T20:59:08.8777198Z             >>> # only single gpu per process is currently supported
2025-03-04T20:59:08.8777440Z             >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2025-03-04T20:59:08.8777566Z             >>>                         sync_bn_network,
2025-03-04T20:59:08.8777718Z             >>>                         device_ids=[args.local_rank],
2025-03-04T20:59:08.8777946Z             >>>                         output_device=args.local_rank)
2025-03-04T20:59:08.8778053Z         
2025-03-04T20:59:08.8778317Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8778425Z     
2025-03-04T20:59:08.8778533Z       warnings.warn(msg)
2025-03-04T20:59:08.8778634Z     
2025-03-04T20:59:08.8778862Z --- Parse Warning: 91 / 116 ---
2025-03-04T20:59:08.8779902Z     /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=825.
2025-03-04T20:59:08.8780223Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8780555Z     Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2025-03-04T20:59:08.8780644Z     
2025-03-04T20:59:08.8780751Z             Args:
2025-03-04T20:59:08.8781010Z                 module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2025-03-04T20:59:08.8781248Z                 process_group (optional): process group to scope synchronization,
2025-03-04T20:59:08.8781373Z                     default is the whole world
2025-03-04T20:59:08.8781474Z     
2025-03-04T20:59:08.8781569Z             Returns:
2025-03-04T20:59:08.8781844Z                 The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2025-03-04T20:59:08.8782070Z                 layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2025-03-04T20:59:08.8782303Z                 a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2025-03-04T20:59:08.8782405Z                 instead.
2025-03-04T20:59:08.8782541Z     
2025-03-04T20:59:08.8782646Z             Example::
2025-03-04T20:59:08.8782743Z     
2025-03-04T20:59:08.8782875Z                 >>> # Network with nn.BatchNorm layer
2025-03-04T20:59:08.8783056Z                 >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:08.8783184Z                 >>> module = torch.nn.Sequential(
2025-03-04T20:59:08.8783323Z                 >>>            torch.nn.Linear(20, 100),
2025-03-04T20:59:08.8783455Z                 >>>            torch.nn.BatchNorm1d(100),
2025-03-04T20:59:08.8783570Z                 >>>          ).cuda()
2025-03-04T20:59:08.8783708Z                 >>> # creating process group (optional)
2025-03-04T20:59:08.8783875Z                 >>> # ranks is a list of int identifying rank ids.
2025-03-04T20:59:08.8783990Z                 >>> ranks = list(range(8))
2025-03-04T20:59:08.8784121Z                 >>> r1, r2 = ranks[:4], ranks[4:]
2025-03-04T20:59:08.8784278Z                 >>> # Note: every rank calls into new_group for every
2025-03-04T20:59:08.8784453Z                 >>> # process group created, even if that rank is not
2025-03-04T20:59:08.8784564Z                 >>> # part of the group.
2025-03-04T20:59:08.8784710Z                 >>> # xdoctest: +SKIP("distributed")
2025-03-04T20:59:08.8784970Z                 >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-03-04T20:59:08.8785198Z                 >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-03-04T20:59:08.8785503Z                 >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2025-03-04T20:59:08.8785629Z     
2025-03-04T20:59:08.8785724Z             
2025-03-04T20:59:08.8785999Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8786085Z     
2025-03-04T20:59:08.8786208Z       warnings.warn(msg)
2025-03-04T20:59:08.8786296Z     
2025-03-04T20:59:08.8786506Z --- Parse Warning: 92 / 116 ---
2025-03-04T20:59:08.8787392Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60.
2025-03-04T20:59:08.8787677Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8787764Z     
2025-03-04T20:59:08.8788094Z     Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2025-03-04T20:59:08.8788181Z     
2025-03-04T20:59:08.8788477Z     * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2025-03-04T20:59:08.8788711Z       be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2025-03-04T20:59:08.8788809Z     
2025-03-04T20:59:08.8789131Z     * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2025-03-04T20:59:08.8789428Z       a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2025-03-04T20:59:08.8789607Z       (tuple of `(name, size)` tuples) for `NamedTensor` input.
2025-03-04T20:59:08.8789706Z     
2025-03-04T20:59:08.8789798Z     Shape:
2025-03-04T20:59:08.8790040Z         - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2025-03-04T20:59:08.8790298Z           dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2025-03-04T20:59:08.8790533Z         - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2025-03-04T20:59:08.8790671Z           :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2025-03-04T20:59:08.8790768Z     
2025-03-04T20:59:08.8790858Z     Args:
2025-03-04T20:59:08.8791029Z         dim (Union[int, str]): Dimension to be unflattened
2025-03-04T20:59:08.8791384Z         unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2025-03-04T20:59:08.8791521Z     
2025-03-04T20:59:08.8791617Z     Examples:
2025-03-04T20:59:08.8791733Z         >>> input = torch.randn(2, 50)
2025-03-04T20:59:08.8791854Z         >>> # With tuple of ints
2025-03-04T20:59:08.8791962Z         >>> m = nn.Sequential(
2025-03-04T20:59:08.8792104Z         >>>     nn.Linear(50, 50),
2025-03-04T20:59:08.8792220Z         >>>     nn.Unflatten(1, (2, 5, 5))
2025-03-04T20:59:08.8792322Z         >>> )
2025-03-04T20:59:08.8792426Z         >>> output = m(input)
2025-03-04T20:59:08.8792546Z         >>> output.size()
2025-03-04T20:59:08.8792651Z         torch.Size([2, 2, 5, 5])
2025-03-04T20:59:08.8792769Z         >>> # With torch.Size
2025-03-04T20:59:08.8792876Z         >>> m = nn.Sequential(
2025-03-04T20:59:08.8792997Z         >>>     nn.Linear(50, 50),
2025-03-04T20:59:08.8793131Z         >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2025-03-04T20:59:08.8793236Z         >>> )
2025-03-04T20:59:08.8793341Z         >>> output = m(input)
2025-03-04T20:59:08.8793456Z         >>> output.size()
2025-03-04T20:59:08.8793562Z         torch.Size([2, 2, 5, 5])
2025-03-04T20:59:08.8793703Z         >>> # With namedshape (tuple of tuples)
2025-03-04T20:59:08.8793861Z         >>> input = torch.randn(2, 50, names=('N', 'features'))
2025-03-04T20:59:08.8794095Z         >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
2025-03-04T20:59:08.8794261Z         >>> output = unflatten(input)
2025-03-04T20:59:08.8794404Z         >>> output.size()
2025-03-04T20:59:08.8794549Z         torch.Size([2, 2, 5, 5])
2025-03-04T20:59:08.8794657Z     
2025-03-04T20:59:08.8795008Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8795112Z     
2025-03-04T20:59:08.8795217Z       warnings.warn(msg)
2025-03-04T20:59:08.8795317Z     
2025-03-04T20:59:08.8795532Z --- Parse Warning: 93 / 116 ---
2025-03-04T20:59:08.8796556Z     /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=1700.
2025-03-04T20:59:08.8796831Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8797056Z     Creates a criterion that measures the triplet loss given input
2025-03-04T20:59:08.8797259Z         tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2025-03-04T20:59:08.8797494Z         positive, and negative examples, respectively), and a nonnegative,
2025-03-04T20:59:08.8797751Z         real-valued function ("distance function") used to compute the relationship
2025-03-04T20:59:08.8797996Z         between the anchor and positive example ("positive distance") and the
2025-03-04T20:59:08.8798161Z         anchor and negative example ("negative distance").
2025-03-04T20:59:08.8798261Z     
2025-03-04T20:59:08.8798505Z         The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2025-03-04T20:59:08.8798624Z         can be described as:
2025-03-04T20:59:08.8798713Z     
2025-03-04T20:59:08.8798824Z         .. math::
2025-03-04T20:59:08.8798970Z             \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2025-03-04T20:59:08.8799132Z             l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2025-03-04T20:59:08.8799230Z     
2025-03-04T20:59:08.8799484Z         where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2025-03-04T20:59:08.8799790Z         quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2025-03-04T20:59:08.8800040Z         and :math:`margin` is a nonnegative margin representing the minimum difference
2025-03-04T20:59:08.8800303Z         between the positive and negative distances that is required for the loss to
2025-03-04T20:59:08.8800554Z         be 0.  The input tensors have :math:`N` elements each and can be of any shape
2025-03-04T20:59:08.8800680Z         that the distance function can handle.
2025-03-04T20:59:08.8800768Z     
2025-03-04T20:59:08.8800936Z         If :attr:`reduction` is not ``'none'``
2025-03-04T20:59:08.8801048Z         (default ``'mean'``), then:
2025-03-04T20:59:08.8801147Z     
2025-03-04T20:59:08.8801242Z         .. math::
2025-03-04T20:59:08.8801395Z             \ell(x, y) =
2025-03-04T20:59:08.8801497Z             \begin{cases}
2025-03-04T20:59:08.8801723Z                 \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2025-03-04T20:59:08.8801922Z                 \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2025-03-04T20:59:08.8802030Z             \end{cases}
2025-03-04T20:59:08.8802118Z     
2025-03-04T20:59:08.8802374Z         See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2025-03-04T20:59:08.8802631Z         loss for input tensors using the :math:`l_p` distance as the distance function.
2025-03-04T20:59:08.8802733Z     
2025-03-04T20:59:08.8802824Z         Args:
2025-03-04T20:59:08.8803119Z             distance_function (Callable, optional): A nonnegative, real-valued function that
2025-03-04T20:59:08.8803318Z                 quantifies the closeness of two tensors. If not specified,
2025-03-04T20:59:08.8803511Z                 `nn.PairwiseDistance` will be used.  Default: ``None``
2025-03-04T20:59:08.8803790Z             margin (float, optional): A nonnegative margin representing the minimum difference
2025-03-04T20:59:08.8804083Z                 between the positive and negative distances required for the loss to be 0. Larger
2025-03-04T20:59:08.8804371Z                 margins penalize cases where the negative examples are not distant enough from the
2025-03-04T20:59:08.8804593Z                 anchors, relative to the positives. Default: :math:`1`.
2025-03-04T20:59:08.8804853Z             swap (bool, optional): Whether to use the distance swap described in the paper
2025-03-04T20:59:08.8805134Z                 `Learning shallow convolutional feature descriptors with triplet losses` by
2025-03-04T20:59:08.8805379Z                 V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2025-03-04T20:59:08.8805671Z                 negative example than the anchor is, swaps the positive example and the anchor in
2025-03-04T20:59:08.8805816Z                 the loss computation. Default: ``False``.
2025-03-04T20:59:08.8806120Z             reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2025-03-04T20:59:08.8806313Z                 ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-03-04T20:59:08.8806518Z                 ``'mean'``: the sum of the output will be divided by the number of
2025-03-04T20:59:08.8806766Z                 elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2025-03-04T20:59:08.8806866Z     
2025-03-04T20:59:08.8806955Z     
2025-03-04T20:59:08.8807062Z         Shape:
2025-03-04T20:59:08.8807344Z             - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2025-03-04T20:59:08.8807499Z               as supported by the distance function.
2025-03-04T20:59:08.8807759Z             - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2025-03-04T20:59:08.8807873Z               otherwise.
2025-03-04T20:59:08.8807963Z     
2025-03-04T20:59:08.8808079Z         Examples::
2025-03-04T20:59:08.8808168Z     
2025-03-04T20:59:08.8808294Z         >>> # Initialize embeddings
2025-03-04T20:59:08.8808422Z         >>> embedding = nn.Embedding(1000, 128)
2025-03-04T20:59:08.8808572Z         >>> anchor_ids = torch.randint(0, 1000, (1,))
2025-03-04T20:59:08.8808713Z         >>> positive_ids = torch.randint(0, 1000, (1,))
2025-03-04T20:59:08.8808867Z         >>> negative_ids = torch.randint(0, 1000, (1,))
2025-03-04T20:59:08.8808986Z         >>> anchor = embedding(anchor_ids)
2025-03-04T20:59:08.8809130Z         >>> positive = embedding(positive_ids)
2025-03-04T20:59:08.8809258Z         >>> negative = embedding(negative_ids)
2025-03-04T20:59:08.8809364Z         >>>
2025-03-04T20:59:08.8809508Z         >>> # Built-in Distance Function
2025-03-04T20:59:08.8809621Z         >>> triplet_loss = \
2025-03-04T20:59:08.8809915Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2025-03-04T20:59:08.8810116Z         >>> output = triplet_loss(anchor, positive, negative)
2025-03-04T20:59:08.8810228Z         >>> output.backward()
2025-03-04T20:59:08.8810336Z         >>>
2025-03-04T20:59:08.8810450Z         >>> # Custom Distance Function
2025-03-04T20:59:08.8810573Z         >>> def l_infinity(x1, x2):
2025-03-04T20:59:08.8810740Z         >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2025-03-04T20:59:08.8810843Z         >>>
2025-03-04T20:59:08.8811038Z         >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2025-03-04T20:59:08.8811156Z         >>> triplet_loss = (
2025-03-04T20:59:08.8811447Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2025-03-04T20:59:08.8811624Z         >>> output = triplet_loss(anchor, positive, negative)
2025-03-04T20:59:08.8811732Z         >>> output.backward()
2025-03-04T20:59:08.8811824Z         >>>
2025-03-04T20:59:08.8811964Z         >>> # Custom Distance Function (Lambda)
2025-03-04T20:59:08.8812069Z         >>> triplet_loss = (
2025-03-04T20:59:08.8812222Z         >>>     nn.TripletMarginWithDistanceLoss(
2025-03-04T20:59:08.8812448Z         >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2025-03-04T20:59:08.8812618Z         >>> output = triplet_loss(anchor, positive, negative)
2025-03-04T20:59:08.8812724Z         >>> output.backward()
2025-03-04T20:59:08.8812848Z     
2025-03-04T20:59:08.8812949Z         Reference:
2025-03-04T20:59:08.8813277Z             V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2025-03-04T20:59:08.8813511Z             https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html
2025-03-04T20:59:08.8813611Z         
2025-03-04T20:59:08.8813876Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2025-03-04T20:59:08.8813976Z     
2025-03-04T20:59:08.8814081Z       warnings.warn(msg)
2025-03-04T20:59:08.8814186Z     
2025-03-04T20:59:08.8814393Z --- Parse Warning: 94 / 116 ---
2025-03-04T20:59:08.8815315Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395.
2025-03-04T20:59:08.8815582Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8815762Z     Computes a partial inverse of :class:`MaxPool2d`.
2025-03-04T20:59:08.8815849Z     
2025-03-04T20:59:08.8816133Z         :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2025-03-04T20:59:08.8816249Z     
2025-03-04T20:59:08.8816497Z         :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2025-03-04T20:59:08.8816748Z         including the indices of the maximal values and computes a partial inverse
2025-03-04T20:59:08.8816921Z         in which all non-maximal values are set to zero.
2025-03-04T20:59:08.8817009Z     
2025-03-04T20:59:08.8817118Z         Note:
2025-03-04T20:59:08.8817444Z             This operation may behave nondeterministically when the input indices has repeat values.
2025-03-04T20:59:08.8818119Z             See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2025-03-04T20:59:08.8818207Z     
2025-03-04T20:59:08.8818467Z         .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2025-03-04T20:59:08.8818652Z                   sizes. Hence, the inversion process can get ambiguous.
2025-03-04T20:59:08.8818864Z                   To accommodate this, you can provide the needed output size
2025-03-04T20:59:08.8819082Z                   as an additional argument :attr:`output_size` in the forward call.
2025-03-04T20:59:08.8819266Z                   See the Inputs and Example below.
2025-03-04T20:59:08.8819352Z     
2025-03-04T20:59:08.8819455Z         Args:
2025-03-04T20:59:08.8819670Z             kernel_size (int or tuple): Size of the max pooling window.
2025-03-04T20:59:08.8819862Z             stride (int or tuple): Stride of the max pooling window.
2025-03-04T20:59:08.8820006Z                 It is set to :attr:`kernel_size` by default.
2025-03-04T20:59:08.8820210Z             padding (int or tuple): Padding that was added to the input
2025-03-04T20:59:08.8820297Z     
2025-03-04T20:59:08.8820402Z         Inputs:
2025-03-04T20:59:08.8820531Z             - `input`: the input Tensor to invert
2025-03-04T20:59:08.8820756Z             - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2025-03-04T20:59:08.8820921Z             - `output_size` (optional): the targeted output size
2025-03-04T20:59:08.8821018Z     
2025-03-04T20:59:08.8821109Z         Shape:
2025-03-04T20:59:08.8821314Z             - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2025-03-04T20:59:08.8821534Z             - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2025-03-04T20:59:08.8821635Z     
2025-03-04T20:59:08.8821737Z               .. math::
2025-03-04T20:59:08.8822027Z                 H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2025-03-04T20:59:08.8822115Z     
2025-03-04T20:59:08.8822224Z               .. math::
2025-03-04T20:59:08.8822519Z                 W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2025-03-04T20:59:08.8822619Z     
2025-03-04T20:59:08.8822791Z               or as given by :attr:`output_size` in the call operator
2025-03-04T20:59:08.8822889Z     
2025-03-04T20:59:08.8822985Z         Example::
2025-03-04T20:59:08.8823072Z     
2025-03-04T20:59:08.8823254Z             >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2025-03-04T20:59:08.8823390Z             >>> unpool = nn.MaxUnpool2d(2, stride=2)
2025-03-04T20:59:08.8823598Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2025-03-04T20:59:08.8823746Z                                         [ 5.,  6.,  7.,  8.],
2025-03-04T20:59:08.8823879Z                                         [ 9., 10., 11., 12.],
2025-03-04T20:59:08.8823995Z                                         [13., 14., 15., 16.]]]])
2025-03-04T20:59:08.8824131Z             >>> output, indices = pool(input)
2025-03-04T20:59:08.8824243Z             >>> unpool(output, indices)
2025-03-04T20:59:08.8824367Z             tensor([[[[  0.,   0.,   0.,   0.],
2025-03-04T20:59:08.8824473Z                       [  0.,   6.,   0.,   8.],
2025-03-04T20:59:08.8824586Z                       [  0.,   0.,   0.,   0.],
2025-03-04T20:59:08.8824694Z                       [  0.,  14.,   0.,  16.]]]])
2025-03-04T20:59:08.8824961Z             >>> # Now using output_size to resolve an ambiguous size for the inverse
2025-03-04T20:59:08.8825110Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2025-03-04T20:59:08.8825238Z                                         [ 6.,  7.,  8.,  9., 10.],
2025-03-04T20:59:08.8825355Z                                         [11., 12., 13., 14., 15.],
2025-03-04T20:59:08.8825487Z                                         [16., 17., 18., 19., 20.]]]])
2025-03-04T20:59:08.8825609Z             >>> output, indices = pool(input)
2025-03-04T20:59:08.8825800Z             >>> # This call will not work without specifying output_size
2025-03-04T20:59:08.8825965Z             >>> unpool(output, indices, output_size=input.size())
2025-03-04T20:59:08.8826093Z             tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2025-03-04T20:59:08.8826200Z                       [ 0.,  7.,  0.,  9.,  0.],
2025-03-04T20:59:08.8826317Z                       [ 0.,  0.,  0.,  0.,  0.],
2025-03-04T20:59:08.8826428Z                       [ 0., 17.,  0., 19.,  0.]]]])
2025-03-04T20:59:08.8826528Z     
2025-03-04T20:59:08.8826640Z     
2025-03-04T20:59:08.8826742Z         
2025-03-04T20:59:08.8827006Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8827107Z     
2025-03-04T20:59:08.8827218Z       warnings.warn(msg)
2025-03-04T20:59:08.8827344Z     
2025-03-04T20:59:08.8827569Z --- Parse Warning: 95 / 116 ---
2025-03-04T20:59:08.8828487Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270.
2025-03-04T20:59:08.8828755Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8829094Z     Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2025-03-04T20:59:08.8829183Z     
2025-03-04T20:59:08.8829527Z         For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2025-03-04T20:59:08.8829645Z         and with 2D inputs, this class
2025-03-04T20:59:08.8829744Z     
2025-03-04T20:59:08.8830057Z             * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2025-03-04T20:59:08.8830390Z             * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2025-03-04T20:59:08.8830697Z             * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2025-03-04T20:59:08.8830796Z     
2025-03-04T20:59:08.8831182Z         However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2025-03-04T20:59:08.8831292Z         operations.
2025-03-04T20:59:08.8831378Z     
2025-03-04T20:59:08.8831659Z         EmbeddingBag also supports per-sample weights as an argument to the forward
2025-03-04T20:59:08.8831905Z         pass. This scales the output of the Embedding before performing a weighted
2025-03-04T20:59:08.8832177Z         reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2025-03-04T20:59:08.8832419Z         only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2025-03-04T20:59:08.8832551Z         :attr:`per_sample_weights`.
2025-03-04T20:59:08.8832640Z     
2025-03-04T20:59:08.8832744Z         Args:
2025-03-04T20:59:08.8832940Z             num_embeddings (int): size of the dictionary of embeddings
2025-03-04T20:59:08.8833129Z             embedding_dim (int): the size of each embedding vector
2025-03-04T20:59:08.8833451Z             max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2025-03-04T20:59:08.8833624Z                                         is renormalized to have norm :attr:`max_norm`.
2025-03-04T20:59:08.8833972Z             norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2025-03-04T20:59:08.8834349Z             scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2025-03-04T20:59:08.8834522Z                                                     the words in the mini-batch. Default ``False``.
2025-03-04T20:59:08.8834734Z                                                     Note: this option is not supported when ``mode="max"``.
2025-03-04T20:59:08.8834992Z             mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2025-03-04T20:59:08.8835231Z                                      ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2025-03-04T20:59:08.8835464Z                                      into consideration. ``"mean"`` computes the average of the values
2025-03-04T20:59:08.8835659Z                                      in the bag, ``"max"`` computes the max value over each bag.
2025-03-04T20:59:08.8835786Z                                      Default: ``"mean"``
2025-03-04T20:59:08.8836128Z             sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2025-03-04T20:59:08.8836437Z                                      Notes for more details regarding sparse gradients. Note: this option is not
2025-03-04T20:59:08.8836610Z                                      supported when ``mode="max"``.
2025-03-04T20:59:08.8836991Z             include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2025-03-04T20:59:08.8837233Z                                           is equivalent to the size of `indices`. This matches the CSR format.
2025-03-04T20:59:08.8837578Z             padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2025-03-04T20:59:08.8837869Z                                          gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2025-03-04T20:59:08.8838126Z                                          during training, i.e. it remains as a fixed "pad". For a newly constructed
2025-03-04T20:59:08.8838426Z                                          EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2025-03-04T20:59:08.8838697Z                                          zeros, but can be updated to another value to be used as the padding vector.
2025-03-04T20:59:08.8838956Z                                          Note that the embedding vector at :attr:`padding_idx` is excluded from the
2025-03-04T20:59:08.8839087Z                                          reduction.
2025-03-04T20:59:08.8839179Z     
2025-03-04T20:59:08.8839292Z         Attributes:
2025-03-04T20:59:08.8839652Z             weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2025-03-04T20:59:08.8839823Z                              initialized from :math:`\mathcal{N}(0, 1)`.
2025-03-04T20:59:08.8839921Z     
2025-03-04T20:59:08.8840039Z         Examples::
2025-03-04T20:59:08.8840131Z     
2025-03-04T20:59:08.8840314Z             >>> # an EmbeddingBag module containing 10 tensors of size 3
2025-03-04T20:59:08.8840498Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2025-03-04T20:59:08.8840641Z             >>> # a batch of 2 samples of 4 indices each
2025-03-04T20:59:08.8840852Z             >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2025-03-04T20:59:08.8841012Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-04T20:59:08.8841179Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-03-04T20:59:08.8841305Z             >>> embedding_sum(input, offsets)
2025-03-04T20:59:08.8841441Z             tensor([[-0.8861, -5.4350, -0.0523],
2025-03-04T20:59:08.8841556Z                     [ 1.1306, -2.5798, -1.0044]])
2025-03-04T20:59:08.8841661Z     
2025-03-04T20:59:08.8841783Z             >>> # Example with padding_idx
2025-03-04T20:59:08.8842047Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2025-03-04T20:59:08.8842245Z             >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2025-03-04T20:59:08.8842420Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-03-04T20:59:08.8842546Z             >>> embedding_sum(input, offsets)
2025-03-04T20:59:08.8842675Z             tensor([[ 0.0000,  0.0000,  0.0000],
2025-03-04T20:59:08.8842788Z                     [-0.7082,  3.2145, -2.6251]])
2025-03-04T20:59:08.8842892Z     
2025-03-04T20:59:08.8843081Z             >>> # An EmbeddingBag can be loaded from an Embedding like so
2025-03-04T20:59:08.8843254Z             >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2025-03-04T20:59:08.8843424Z             >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2025-03-04T20:59:08.8843553Z                     embedding.weight,
2025-03-04T20:59:08.8843706Z                     padding_idx=embedding.padding_idx,
2025-03-04T20:59:08.8843827Z                     mode='sum')
2025-03-04T20:59:08.8843921Z         
2025-03-04T20:59:08.8844230Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8844319Z     
2025-03-04T20:59:08.8844439Z       warnings.warn(msg)
2025-03-04T20:59:08.8844526Z     
2025-03-04T20:59:08.8844772Z --- Parse Warning: 96 / 116 ---
2025-03-04T20:59:08.8845798Z     /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=1742.
2025-03-04T20:59:08.8846089Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8846177Z     
2025-03-04T20:59:08.8846438Z     Context manager for training with uneven inputs across processes in DDP.
2025-03-04T20:59:08.8846530Z     
2025-03-04T20:59:08.8846777Z     This context manager will keep track of already-joined DDP processes,
2025-03-04T20:59:08.8846992Z     and "shadow" the forward and backward passes by inserting collective
2025-03-04T20:59:08.8847246Z     communication operations to match with the ones created by non-joined
2025-03-04T20:59:08.8847487Z     DDP processes. This will ensure each collective call has a corresponding
2025-03-04T20:59:08.8847729Z     call by already-joined DDP processes, preventing hangs or errors that
2025-03-04T20:59:08.8847939Z     would otherwise happen when training with uneven inputs across
2025-03-04T20:59:08.8848196Z     processes. Alternatively, if the flag ``throw_on_early_termination`` is
2025-03-04T20:59:08.8848415Z     specified to be ``True``, all trainers will throw an error once one rank
2025-03-04T20:59:08.8848659Z     runs out of inputs, allowing these errors to be caught and handled
2025-03-04T20:59:08.8848781Z     according to application logic.
2025-03-04T20:59:08.8848885Z     
2025-03-04T20:59:08.8849115Z     Once all DDP processes have joined, the context manager will broadcast
2025-03-04T20:59:08.8849363Z     the model corresponding to the last joined process to all processes to
2025-03-04T20:59:08.8849526Z     ensure the model is the same across all processes
2025-03-04T20:59:08.8849653Z     (which is guaranteed by DDP).
2025-03-04T20:59:08.8849742Z     
2025-03-04T20:59:08.8849966Z     To use this to enable training with uneven inputs across processes,
2025-03-04T20:59:08.8850202Z     simply wrap this context manager around your training loop. No further
2025-03-04T20:59:08.8850401Z     modifications to the model or data loading is required.
2025-03-04T20:59:08.8850490Z     
2025-03-04T20:59:08.8850603Z     .. warning::
2025-03-04T20:59:08.8850828Z         If the model or training loop this context manager is wrapped around
2025-03-04T20:59:08.8851035Z         has additional distributed collective operations, such as
2025-03-04T20:59:08.8851231Z         ``SyncBatchNorm`` in the model's forward pass, then the flag
2025-03-04T20:59:08.8851488Z         ``throw_on_early_termination`` must be enabled. This is because this
2025-03-04T20:59:08.8851715Z         context manager is not aware of non-DDP collective communication.
2025-03-04T20:59:08.8851906Z         This flag will cause all ranks to throw when any one rank
2025-03-04T20:59:08.8852127Z         exhausts inputs, allowing these errors to be caught and recovered
2025-03-04T20:59:08.8852248Z         from across all ranks.
2025-03-04T20:59:08.8852337Z     
2025-03-04T20:59:08.8852441Z     Args:
2025-03-04T20:59:08.8852633Z         divide_by_initial_world_size (bool): If ``True``, will divide
2025-03-04T20:59:08.8852860Z             gradients by the initial ``world_size`` DDP training was launched
2025-03-04T20:59:08.8853038Z             with. If ``False``, will compute the effective world size
2025-03-04T20:59:08.8853248Z             (number of ranks that have not depleted their inputs yet) and
2025-03-04T20:59:08.8853406Z             divide gradients by that during allreduce. Set
2025-03-04T20:59:08.8853608Z             ``divide_by_initial_world_size=True`` to ensure every input
2025-03-04T20:59:08.8853850Z             sample including the uneven inputs have equal weight in terms of
2025-03-04T20:59:08.8854042Z             how much they contribute to the global gradient. This is
2025-03-04T20:59:08.8854249Z             achieved by always dividing the gradient by the initial
2025-03-04T20:59:08.8854460Z             ``world_size`` even when we encounter uneven inputs. If you set
2025-03-04T20:59:08.8854638Z             this to ``False``, we divide the gradient by the remaining
2025-03-04T20:59:08.8854854Z             number of nodes. This ensures parity with training on a smaller
2025-03-04T20:59:08.8855048Z             ``world_size`` although it also means the uneven inputs would
2025-03-04T20:59:08.8855262Z             contribute more towards the global gradient. Typically, you
2025-03-04T20:59:08.8855459Z             would want to set this to ``True`` for cases where the last few
2025-03-04T20:59:08.8855679Z             inputs of your training job are uneven. In extreme cases, where
2025-03-04T20:59:08.8855875Z             there is a large discrepancy in the number of inputs, setting
2025-03-04T20:59:08.8856041Z             this to ``False`` might provide better results.
2025-03-04T20:59:08.8856263Z         enable (bool): Whether to enable uneven input detection or not. Pass
2025-03-04T20:59:08.8856454Z             in ``enable=False`` to disable in cases where you know that
2025-03-04T20:59:08.8856650Z             inputs are even across participating processes. Default is
2025-03-04T20:59:08.8856756Z             ``True``.
2025-03-04T20:59:08.8856948Z         throw_on_early_termination (bool): Whether to throw an error
2025-03-04T20:59:08.8857173Z             or continue training when at least one rank has exhausted
2025-03-04T20:59:08.8857367Z             inputs. If ``True``, will throw upon the first rank reaching end
2025-03-04T20:59:08.8857556Z             of data. If ``False``, will continue training with a smaller
2025-03-04T20:59:08.8857842Z             effective world size until all ranks are joined. Note that if
2025-03-04T20:59:08.8857996Z             this flag is specified, then the flag
2025-03-04T20:59:08.8858179Z             ``divide_by_initial_world_size`` would be ignored. Default
2025-03-04T20:59:08.8858291Z             is ``False``.
2025-03-04T20:59:08.8858384Z     
2025-03-04T20:59:08.8858473Z     
2025-03-04T20:59:08.8858586Z     Example::
2025-03-04T20:59:08.8858674Z     
2025-03-04T20:59:08.8858814Z         >>> # xdoctest: +SKIP("Distributed")
2025-03-04T20:59:08.8858914Z         >>> import torch
2025-03-04T20:59:08.8859058Z         >>> import torch.distributed as dist
2025-03-04T20:59:08.8859157Z         >>> import os
2025-03-04T20:59:08.8859304Z         >>> import torch.multiprocessing as mp
2025-03-04T20:59:08.8859413Z         >>> import torch.nn as nn
2025-03-04T20:59:08.8859536Z         >>> # On each spawned worker
2025-03-04T20:59:08.8859671Z         >>> def worker(rank):
2025-03-04T20:59:08.8859869Z         >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2025-03-04T20:59:08.8859990Z         >>>     torch.cuda.set_device(rank)
2025-03-04T20:59:08.8860144Z         >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2025-03-04T20:59:08.8860328Z         >>>     model = torch.nn.parallel.DistributedDataParallel(
2025-03-04T20:59:08.8860489Z         >>>         model, device_ids=[rank], output_device=rank
2025-03-04T20:59:08.8860580Z         >>>     )
2025-03-04T20:59:08.8860730Z         >>>     # Rank 1 gets one more input than rank 0.
2025-03-04T20:59:08.8860926Z         >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2025-03-04T20:59:08.8861046Z         >>>     with model.join():
2025-03-04T20:59:08.8861156Z         >>>         for _ in range(5):
2025-03-04T20:59:08.8861281Z         >>>             for inp in inputs:
2025-03-04T20:59:08.8861403Z         >>>                 loss = model(inp).sum()
2025-03-04T20:59:08.8861529Z         >>>                 loss.backward()
2025-03-04T20:59:08.8861728Z         >>>     # Without the join() API, the below synchronization will hang
2025-03-04T20:59:08.8861922Z         >>>     # blocking for rank 1's allreduce to complete.
2025-03-04T20:59:08.8862056Z         >>>     torch.cuda.synchronize(device=rank)
2025-03-04T20:59:08.8862156Z     
2025-03-04T20:59:08.8862445Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8862545Z     
2025-03-04T20:59:08.8862651Z       warnings.warn(msg)
2025-03-04T20:59:08.8862750Z     
2025-03-04T20:59:08.8862963Z --- Parse Warning: 97 / 116 ---
2025-03-04T20:59:08.8864080Z     /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=2033.
2025-03-04T20:59:08.8864352Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8864456Z     
2025-03-04T20:59:08.8864776Z     Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2025-03-04T20:59:08.8864880Z     
2025-03-04T20:59:08.8865097Z     Registers an optimizer with DDP such that the optimization for a
2025-03-04T20:59:08.8865334Z     parameter will run immediately when that parameter's gradient is
2025-03-04T20:59:08.8865549Z     finished with reduction, instead of waiting for all parameters'
2025-03-04T20:59:08.8865790Z     gradients to finish reduction. This can result in a training speedup
2025-03-04T20:59:08.8866021Z     depending on your workload since the optimizer can run while gradient
2025-03-04T20:59:08.8866299Z     reduction for other parameters are still ongoing. In addition, this has
2025-03-04T20:59:08.8866533Z     the potential to reduce peak memory consumption during training, as it
2025-03-04T20:59:08.8866754Z     only needs to load the per-parameter optimizer states of a single
2025-03-04T20:59:08.8866978Z     parameter at a time, instead of loading all per-parameter optimizer
2025-03-04T20:59:08.8867090Z     states at once.
2025-03-04T20:59:08.8867180Z     
2025-03-04T20:59:08.8867283Z     Args:
2025-03-04T20:59:08.8867488Z         optim (Type): a ``torch.optim.Optimizer`` class to be registered
2025-03-04T20:59:08.8867612Z         as a fused optimizer.
2025-03-04T20:59:08.8867788Z         *args (Sequence[Any]): Arguments to forward to `optim`.
2025-03-04T20:59:08.8868022Z         optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2025-03-04T20:59:08.8868254Z         to optimize, similar to `params` argument of traditional `torch.optim`
2025-03-04T20:59:08.8868480Z         Optimizers. If this is omitted, all DDP model parameters will be
2025-03-04T20:59:08.8868575Z         optimized.
2025-03-04T20:59:08.8868782Z         **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2025-03-04T20:59:08.8868884Z     
2025-03-04T20:59:08.8869023Z     .. warning ::
2025-03-04T20:59:08.8869259Z         _register_fused_optim should only be called once on a DDP instance,
2025-03-04T20:59:08.8869481Z         and registering multiple fused optimizers for the same DDP model
2025-03-04T20:59:08.8869634Z         is not currently supported. Please ping
2025-03-04T20:59:08.8869879Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-04T20:59:08.8869998Z         for your use case.
2025-03-04T20:59:08.8870087Z     
2025-03-04T20:59:08.8870200Z     .. warning ::
2025-03-04T20:59:08.8870403Z         _register_fused_optim and register_comm_hook currently do not
2025-03-04T20:59:08.8870644Z         compose together, meaning that custom DDP communication hooks are
2025-03-04T20:59:08.8870829Z         not supported with overlapped optimizers. Please ping
2025-03-04T20:59:08.8871082Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-04T20:59:08.8871188Z         for your use case.
2025-03-04T20:59:08.8871294Z     
2025-03-04T20:59:08.8871392Z     .. warning ::
2025-03-04T20:59:08.8871641Z         Gradient accumulation and DDP `no_sync` are currently not supported
2025-03-04T20:59:08.8871808Z         with overlapped optimizer. Please ping
2025-03-04T20:59:08.8872061Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-03-04T20:59:08.8872191Z         for your use case.
2025-03-04T20:59:08.8872293Z     
2025-03-04T20:59:08.8872393Z     Example::
2025-03-04T20:59:08.8872497Z     
2025-03-04T20:59:08.8872641Z         >>> # xdoctest: +SKIP("No rendezvous handler")
2025-03-04T20:59:08.8872964Z         >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2025-03-04T20:59:08.8873172Z         >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2025-03-04T20:59:08.8873288Z         >>> lr = 1e-2
2025-03-04T20:59:08.8873394Z         >>> betas = (0.9, 0.99)
2025-03-04T20:59:08.8873509Z         >>> eps = 1e-6
2025-03-04T20:59:08.8874100Z         >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2025-03-04T20:59:08.8874252Z         >>> # Example with subset of parameters
2025-03-04T20:59:08.8874404Z         >>> params_to_opt = [list(net.parameters())[0]]
2025-03-04T20:59:08.8874533Z         >>> net._register_fused_optim(
2025-03-04T20:59:08.8874778Z         ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2025-03-04T20:59:08.8874880Z         ... )
2025-03-04T20:59:08.8874967Z     
2025-03-04T20:59:08.8875242Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8875333Z     
2025-03-04T20:59:08.8875451Z       warnings.warn(msg)
2025-03-04T20:59:08.8875538Z     
2025-03-04T20:59:08.8875841Z --- Parse Warning: 98 / 116 ---
2025-03-04T20:59:08.8876860Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6.
2025-03-04T20:59:08.8877146Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8877377Z     Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2025-03-04T20:59:08.8877483Z     
2025-03-04T20:59:08.8877771Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-04T20:59:08.8878066Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-04T20:59:08.8878333Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-04T20:59:08.8878662Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-04T20:59:08.8878752Z     
2025-03-04T20:59:08.8878863Z         .. note::
2025-03-04T20:59:08.8879108Z             Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2025-03-04T20:59:08.8879396Z             than the utility function ``convert_conv2d_weight_memory_format``. Any
2025-03-04T20:59:08.8879618Z             layer with 4d weight will be affected by ``model.to``, which does not
2025-03-04T20:59:08.8879861Z             necessarily benefit from conversion to specified ``memory_format``.
2025-03-04T20:59:08.8880095Z             One place we are confident in is that NHWC(channels_last) conversion for
2025-03-04T20:59:08.8880334Z             convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2025-03-04T20:59:08.8880544Z             even in cases where we have to apply permutation to input tensors.
2025-03-04T20:59:08.8880645Z     
2025-03-04T20:59:08.8880883Z             Hence our strategy here is to convert only the weight of convolution to
2025-03-04T20:59:08.8881024Z             channels_last. This ensures that;
2025-03-04T20:59:08.8881253Z             1. Fast convolution kernels will be used, the benefit of which could
2025-03-04T20:59:08.8881513Z             outweigh overhead of permutation (if input is not in the same format).
2025-03-04T20:59:08.8881764Z             2. No unnecessary permutations are applied on layers that do not benefit
2025-03-04T20:59:08.8881946Z             from memory_format conversion.
2025-03-04T20:59:08.8882034Z     
2025-03-04T20:59:08.8882319Z             The optimal case is that, layers between convolution layers are channels
2025-03-04T20:59:08.8882568Z             last compatible. Input tensor would be permuted to channels last when it
2025-03-04T20:59:08.8882822Z             encounters the first convolution layer and stay in that memory format.
2025-03-04T20:59:08.8883071Z             Hence following convolutions will not need to permute its input tensor.
2025-03-04T20:59:08.8883176Z     
2025-03-04T20:59:08.8883411Z             In case where a channels last incompatible layer is between convolution
2025-03-04T20:59:08.8883647Z             layers, we need to permute the input tensor back to contiguous format
2025-03-04T20:59:08.8883880Z             for that layer. The input tensor will go through the remaining layers in
2025-03-04T20:59:08.8884131Z             contiguous format and be permuted to channels last when it encounters
2025-03-04T20:59:08.8884348Z             another convolution layer. There's no point in propagating that
2025-03-04T20:59:08.8884590Z             permutation to an earlier layer, as most layers are quite agnostic to
2025-03-04T20:59:08.8884696Z             ``memory_format``.
2025-03-04T20:59:08.8884796Z     
2025-03-04T20:59:08.8885036Z             This claim might change when PyTorch supports fusion of permutation, as
2025-03-04T20:59:08.8885278Z             there might have been a better spot to fuse the permutation other than
2025-03-04T20:59:08.8885431Z             immediately before a convolution.
2025-03-04T20:59:08.8885533Z     
2025-03-04T20:59:08.8885626Z         Args:
2025-03-04T20:59:08.8885851Z             module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2025-03-04T20:59:08.8885980Z                                 ``nn.Module``
2025-03-04T20:59:08.8886140Z             memory_format: user specified ``memory_format``,
2025-03-04T20:59:08.8886344Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-04T20:59:08.8886433Z     
2025-03-04T20:59:08.8886540Z         Returns:
2025-03-04T20:59:08.8886695Z             The original module with updated ``nn.Conv2d``
2025-03-04T20:59:08.8886796Z     
2025-03-04T20:59:08.8886890Z         Example:
2025-03-04T20:59:08.8887052Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:08.8887218Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-04T20:59:08.8887470Z             >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda")
2025-03-04T20:59:08.8887587Z             >>> model = nn.Sequential(
2025-03-04T20:59:08.8887726Z             >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2025-03-04T20:59:08.8887840Z             >>> # This is identical to:
2025-03-04T20:59:08.8888131Z             >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-04T20:59:08.8888406Z             >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-03-04T20:59:08.8888529Z             >>> out = model(input)
2025-03-04T20:59:08.8888618Z         
2025-03-04T20:59:08.8888896Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8888985Z     
2025-03-04T20:59:08.8889105Z       warnings.warn(msg)
2025-03-04T20:59:08.8889193Z     
2025-03-04T20:59:08.8889408Z --- Parse Warning: 99 / 116 ---
2025-03-04T20:59:08.8890417Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81.
2025-03-04T20:59:08.8890702Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8890928Z     Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2025-03-04T20:59:08.8891255Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-03-04T20:59:08.8891564Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-03-04T20:59:08.8891842Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2025-03-04T20:59:08.8892161Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-03-04T20:59:08.8892261Z     
2025-03-04T20:59:08.8892355Z         .. note::
2025-03-04T20:59:08.8892623Z             Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2025-03-04T20:59:08.8892855Z             than the utility function ``convert_conv3d_weight_memory_format``. Any
2025-03-04T20:59:08.8893085Z             layer with 4d weight will be affected by ``model.to``, which does not
2025-03-04T20:59:08.8893318Z             necessarily benefit from conversion to specified ``memory_format``.
2025-03-04T20:59:08.8893575Z             One place we are confident in is that NDHWC(channels_last_3d) conversion for
2025-03-04T20:59:08.8893805Z             convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2025-03-04T20:59:08.8894028Z             even in cases where we have to apply permutation to input tensors.
2025-03-04T20:59:08.8894116Z     
2025-03-04T20:59:08.8894362Z             Hence our strategy here is to convert only the weight of convolution to
2025-03-04T20:59:08.8894492Z             channels_last_3d. This ensures that;
2025-03-04T20:59:08.8894758Z             1. Fast convolution kernels will be used, the benefit of which could
2025-03-04T20:59:08.8894999Z             outweigh overhead of permutation (if input is not in the same format).
2025-03-04T20:59:08.8895254Z             2. No unnecessary permutations are applied on layers that do not benefit
2025-03-04T20:59:08.8895379Z             from memory_format conversion.
2025-03-04T20:59:08.8895480Z     
2025-03-04T20:59:08.8895715Z             The optimal case is that, layers between convolution layers are channels
2025-03-04T20:59:08.8895971Z             last compatible. Input tensor would be permuted to channels last when it
2025-03-04T20:59:08.8896212Z             encounters the first convolution layer and stay in that memory format.
2025-03-04T20:59:08.8896469Z             Hence following convolutions will not need to permute its input tensor.
2025-03-04T20:59:08.8896558Z     
2025-03-04T20:59:08.8896800Z             In case where a channels last incompatible layer is between convolution
2025-03-04T20:59:08.8897022Z             layers, we need to permute the input tensor back to contiguous format
2025-03-04T20:59:08.8897266Z             for that layer. The input tensor will go through the remaining layers in
2025-03-04T20:59:08.8897501Z             contiguous format and be permuted to channels last when it encounters
2025-03-04T20:59:08.8897832Z             another convolution layer. There's no point in propagating that
2025-03-04T20:59:08.8898066Z             permutation to an earlier layer, as most layers are quite agnostic to
2025-03-04T20:59:08.8898184Z             ``memory_format``.
2025-03-04T20:59:08.8898271Z     
2025-03-04T20:59:08.8898530Z             This claim might change when PyTorch supports fusion of permutation, as
2025-03-04T20:59:08.8898760Z             there might have been a better spot to fuse the permutation other than
2025-03-04T20:59:08.8898900Z             immediately before a convolution.
2025-03-04T20:59:08.8898990Z     
2025-03-04T20:59:08.8899097Z         Args:
2025-03-04T20:59:08.8899321Z             module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2025-03-04T20:59:08.8899450Z                                 ``nn.Module``
2025-03-04T20:59:08.8899611Z             memory_format: user specified ``memory_format``,
2025-03-04T20:59:08.8899816Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-03-04T20:59:08.8899909Z     
2025-03-04T20:59:08.8900048Z         Returns:
2025-03-04T20:59:08.8900202Z             The original module with updated ``nn.Conv3d``
2025-03-04T20:59:08.8900304Z     
2025-03-04T20:59:08.8900399Z         Example:
2025-03-04T20:59:08.8900584Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-03-04T20:59:08.8900750Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-03-04T20:59:08.8901004Z             >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda")
2025-03-04T20:59:08.8901118Z             >>> model = nn.Sequential(
2025-03-04T20:59:08.8901256Z             >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2025-03-04T20:59:08.8901371Z             >>> # This is identical to:
2025-03-04T20:59:08.8901647Z             >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-04T20:59:08.8901931Z             >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-03-04T20:59:08.8902050Z             >>> out = model(input)
2025-03-04T20:59:08.8902143Z         
2025-03-04T20:59:08.8902417Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8902506Z     
2025-03-04T20:59:08.8902624Z       warnings.warn(msg)
2025-03-04T20:59:08.8902712Z     
2025-03-04T20:59:08.8902936Z --- Parse Warning: 100 / 116 ---
2025-03-04T20:59:08.8903841Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=935.
2025-03-04T20:59:08.8904182Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8904423Z     Prune tensor by removing random channels along the specified dimension.
2025-03-04T20:59:08.8904530Z     
2025-03-04T20:59:08.8904778Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-04T20:59:08.8905020Z         by removing the specified ``amount`` of (currently unpruned) channels
2025-03-04T20:59:08.8905172Z         along the specified ``dim`` selected at random.
2025-03-04T20:59:08.8905398Z         Modifies module in place (and also return the modified module)
2025-03-04T20:59:08.8905490Z         by:
2025-03-04T20:59:08.8905595Z     
2025-03-04T20:59:08.8905810Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:08.8906053Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:08.8906272Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:08.8906504Z            original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:08.8906608Z            ``name+'_orig'``.
2025-03-04T20:59:08.8906698Z     
2025-03-04T20:59:08.8906832Z         Args:
2025-03-04T20:59:08.8907018Z             module (nn.Module): module containing the tensor to prune
2025-03-04T20:59:08.8907224Z             name (str): parameter name within ``module`` on which pruning
2025-03-04T20:59:08.8907329Z                     will act.
2025-03-04T20:59:08.8907522Z             amount (int or float): quantity of parameters to prune.
2025-03-04T20:59:08.8907711Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-04T20:59:08.8907938Z                 fraction of parameters to prune. If ``int``, it represents the
2025-03-04T20:59:08.8908080Z                 absolute number of parameters to prune.
2025-03-04T20:59:08.8908304Z             dim (int): index of the dim along which we define channels to prune.
2025-03-04T20:59:08.8908397Z     
2025-03-04T20:59:08.8908504Z         Returns:
2025-03-04T20:59:08.8908734Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-04T20:59:08.8908839Z     
2025-03-04T20:59:08.8908938Z         Examples:
2025-03-04T20:59:08.8909064Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.8909190Z             >>> m = prune.random_structured(
2025-03-04T20:59:08.8909377Z             ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
2025-03-04T20:59:08.8909467Z             ... )
2025-03-04T20:59:08.8909694Z             >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
2025-03-04T20:59:08.8909812Z             >>> print(columns_pruned)
2025-03-04T20:59:08.8909915Z             3
2025-03-04T20:59:08.8910004Z         
2025-03-04T20:59:08.8910278Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8910364Z     
2025-03-04T20:59:08.8910479Z       warnings.warn(msg)
2025-03-04T20:59:08.8910566Z     
2025-03-04T20:59:08.8910780Z --- Parse Warning: 101 / 116 ---
2025-03-04T20:59:08.8911666Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=976.
2025-03-04T20:59:08.8911950Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8912265Z     Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2025-03-04T20:59:08.8912367Z     
2025-03-04T20:59:08.8912611Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-03-04T20:59:08.8912850Z         by removing the specified ``amount`` of (currently unpruned) channels
2025-03-04T20:59:08.8913034Z         along the specified ``dim`` with the lowest L\ ``n``-norm.
2025-03-04T20:59:08.8913252Z         Modifies module in place (and also return the modified module)
2025-03-04T20:59:08.8913435Z         by:
2025-03-04T20:59:08.8913536Z     
2025-03-04T20:59:08.8913754Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:08.8913997Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:08.8914217Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:08.8914448Z            original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:08.8914553Z            ``name+'_orig'``.
2025-03-04T20:59:08.8914652Z     
2025-03-04T20:59:08.8914746Z         Args:
2025-03-04T20:59:08.8914945Z             module (nn.Module): module containing the tensor to prune
2025-03-04T20:59:08.8915137Z             name (str): parameter name within ``module`` on which pruning
2025-03-04T20:59:08.8915252Z                     will act.
2025-03-04T20:59:08.8915432Z             amount (int or float): quantity of parameters to prune.
2025-03-04T20:59:08.8915631Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-04T20:59:08.8915843Z                 fraction of parameters to prune. If ``int``, it represents the
2025-03-04T20:59:08.8915994Z                 absolute number of parameters to prune.
2025-03-04T20:59:08.8916224Z             n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2025-03-04T20:59:08.8916400Z                 entries for argument ``p`` in :func:`torch.norm`.
2025-03-04T20:59:08.8916608Z             dim (int): index of the dim along which we define channels to prune.
2025-03-04T20:59:08.8916863Z             importance_scores (torch.Tensor): tensor of importance scores (of same
2025-03-04T20:59:08.8917060Z                 shape as module parameter) used to compute mask for pruning.
2025-03-04T20:59:08.8917303Z                 The values in this tensor indicate the importance of the corresponding
2025-03-04T20:59:08.8917442Z                 elements in the parameter being pruned.
2025-03-04T20:59:08.8917691Z                 If unspecified or None, the module parameter will be used in its place.
2025-03-04T20:59:08.8917778Z     
2025-03-04T20:59:08.8917886Z         Returns:
2025-03-04T20:59:08.8918114Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-04T20:59:08.8918216Z     
2025-03-04T20:59:08.8918312Z         Examples:
2025-03-04T20:59:08.8918468Z             >>> from torch.nn.utils import prune
2025-03-04T20:59:08.8918596Z             >>> m = prune.ln_structured(
2025-03-04T20:59:08.8918809Z             ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
2025-03-04T20:59:08.8918914Z             ... )
2025-03-04T20:59:08.8919003Z         
2025-03-04T20:59:08.8919276Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8919363Z     
2025-03-04T20:59:08.8919482Z       warnings.warn(msg)
2025-03-04T20:59:08.8919569Z     
2025-03-04T20:59:08.8919779Z --- Parse Warning: 102 / 116 ---
2025-03-04T20:59:08.8920692Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1023.
2025-03-04T20:59:08.8920976Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8921065Z     
2025-03-04T20:59:08.8921516Z     Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2025-03-04T20:59:08.8921603Z     
2025-03-04T20:59:08.8921732Z     Modifies modules in place by:
2025-03-04T20:59:08.8921819Z     
2025-03-04T20:59:08.8922048Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:08.8922276Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:08.8922508Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:08.8922749Z        original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:08.8922862Z        ``name+'_orig'``.
2025-03-04T20:59:08.8922950Z     
2025-03-04T20:59:08.8923053Z     Args:
2025-03-04T20:59:08.8923266Z         parameters (Iterable of (module, name) tuples): parameters of
2025-03-04T20:59:08.8923481Z             the model to prune in a global fashion, i.e. by aggregating all
2025-03-04T20:59:08.8923698Z             weights prior to deciding which ones to prune. module must be of
2025-03-04T20:59:08.8923872Z             type :class:`nn.Module`, and name must be a string.
2025-03-04T20:59:08.8924108Z         pruning_method (function): a valid pruning function from this module,
2025-03-04T20:59:08.8924386Z             or a custom one implemented by the user that satisfies the
2025-03-04T20:59:08.8924635Z             implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2025-03-04T20:59:08.8924884Z         importance_scores (dict): a dictionary mapping (module, name) tuples to
2025-03-04T20:59:08.8925122Z             the corresponding parameter's importance scores tensor. The tensor
2025-03-04T20:59:08.8925354Z             should be the same shape as the parameter, and is used for computing
2025-03-04T20:59:08.8925492Z             mask for pruning.
2025-03-04T20:59:08.8925719Z             If unspecified or None, the parameter will be used in place of its
2025-03-04T20:59:08.8925828Z             importance scores.
2025-03-04T20:59:08.8925973Z         kwargs: other keyword arguments such as:
2025-03-04T20:59:08.8926178Z             amount (int or float): quantity of parameters to prune across the
2025-03-04T20:59:08.8926309Z             specified parameters.
2025-03-04T20:59:08.8926491Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-03-04T20:59:08.8926710Z             fraction of parameters to prune. If ``int``, it represents the
2025-03-04T20:59:08.8926849Z             absolute number of parameters to prune.
2025-03-04T20:59:08.8926953Z     
2025-03-04T20:59:08.8927047Z     Raises:
2025-03-04T20:59:08.8927221Z         TypeError: if ``PRUNING_TYPE != 'unstructured'``
2025-03-04T20:59:08.8927310Z     
2025-03-04T20:59:08.8927416Z     Note:
2025-03-04T20:59:08.8927640Z         Since global structured pruning doesn't make much sense unless the
2025-03-04T20:59:08.8927858Z         norm is normalized by the size of the parameter, we now limit the
2025-03-04T20:59:08.8928044Z         scope of global pruning to unstructured methods.
2025-03-04T20:59:08.8928140Z     
2025-03-04T20:59:08.8928235Z     Examples:
2025-03-04T20:59:08.8928394Z         >>> from torch.nn.utils import prune
2025-03-04T20:59:08.8928525Z         >>> from collections import OrderedDict
2025-03-04T20:59:08.8928661Z         >>> net = nn.Sequential(OrderedDict([
2025-03-04T20:59:08.8928777Z         ...     ('first', nn.Linear(10, 4)),
2025-03-04T20:59:08.8928903Z         ...     ('second', nn.Linear(4, 1)),
2025-03-04T20:59:08.8928994Z         ... ]))
2025-03-04T20:59:08.8929108Z         >>> parameters_to_prune = (
2025-03-04T20:59:08.8929233Z         ...     (net.first, 'weight'),
2025-03-04T20:59:08.8929344Z         ...     (net.second, 'weight'),
2025-03-04T20:59:08.8929447Z         ... )
2025-03-04T20:59:08.8929567Z         >>> prune.global_unstructured(
2025-03-04T20:59:08.8929690Z         ...     parameters_to_prune,
2025-03-04T20:59:08.8929834Z         ...     pruning_method=prune.L1Unstructured,
2025-03-04T20:59:08.8929951Z         ...     amount=10,
2025-03-04T20:59:08.8930041Z         ... )
2025-03-04T20:59:08.8930284Z         >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2025-03-04T20:59:08.8930383Z         tensor(10)
2025-03-04T20:59:08.8930485Z     
2025-03-04T20:59:08.8930573Z     
2025-03-04T20:59:08.8930843Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8930932Z     
2025-03-04T20:59:08.8931050Z       warnings.warn(msg)
2025-03-04T20:59:08.8931138Z     
2025-03-04T20:59:08.8931390Z --- Parse Warning: 103 / 116 ---
2025-03-04T20:59:08.8932288Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1142.
2025-03-04T20:59:08.8932575Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8932984Z     Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2025-03-04T20:59:08.8933086Z     
2025-03-04T20:59:08.8933485Z         Modifies module in place (and also return the modified module) by:
2025-03-04T20:59:08.8933647Z     
2025-03-04T20:59:08.8933922Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-03-04T20:59:08.8938794Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-03-04T20:59:08.8939074Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-03-04T20:59:08.8939310Z            original (unpruned) parameter is stored in a new parameter named
2025-03-04T20:59:08.8939418Z            ``name+'_orig'``.
2025-03-04T20:59:08.8939518Z     
2025-03-04T20:59:08.8939687Z         Args:
2025-03-04T20:59:08.8939891Z             module (nn.Module): module containing the tensor to prune
2025-03-04T20:59:08.8940089Z             name (str): parameter name within ``module`` on which pruning
2025-03-04T20:59:08.8940202Z                 will act.
2025-03-04T20:59:08.8940391Z             mask (Tensor): binary mask to be applied to the parameter.
2025-03-04T20:59:08.8940486Z     
2025-03-04T20:59:08.8940596Z         Returns:
2025-03-04T20:59:08.8940828Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-03-04T20:59:08.8940933Z     
2025-03-04T20:59:08.8941032Z         Examples:
2025-03-04T20:59:08.8941177Z             >>> from torch.nn.utils import prune
2025-03-04T20:59:08.8941301Z             >>> m = prune.custom_from_mask(
2025-03-04T20:59:08.8941499Z             ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
2025-03-04T20:59:08.8941593Z             ... )
2025-03-04T20:59:08.8941713Z             >>> print(m.bias_mask)
2025-03-04T20:59:08.8941819Z             tensor([0., 1., 0.])
2025-03-04T20:59:08.8941919Z     
2025-03-04T20:59:08.8942012Z         
2025-03-04T20:59:08.8942325Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8942415Z     
2025-03-04T20:59:08.8942533Z       warnings.warn(msg)
2025-03-04T20:59:08.8942624Z     
2025-03-04T20:59:08.8942920Z --- Parse Warning: 104 / 116 ---
2025-03-04T20:59:08.8943824Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=117.
2025-03-04T20:59:08.8944111Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8944490Z     Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2025-03-04T20:59:08.8944588Z     
2025-03-04T20:59:08.8944845Z         Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2025-03-04T20:59:08.8945079Z         Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2025-03-04T20:59:08.8945307Z         Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2025-03-04T20:59:08.8945413Z         (UAI 2018).
2025-03-04T20:59:08.8945503Z     
2025-03-04T20:59:08.8945743Z         Exponential Moving Average is a variation of `Polyak averaging`_,
2025-03-04T20:59:08.8945993Z         but using exponential weights instead of equal weights across iterations.
2025-03-04T20:59:08.8946093Z     
2025-03-04T20:59:08.8946341Z         AveragedModel class creates a copy of the provided module :attr:`model`
2025-03-04T20:59:08.8946612Z         on the device :attr:`device` and allows to compute running averages of the
2025-03-04T20:59:08.8946736Z         parameters of the :attr:`model`.
2025-03-04T20:59:08.8946834Z     
2025-03-04T20:59:08.8946925Z         Args:
2025-03-04T20:59:08.8947102Z             model (torch.nn.Module): model to use with SWA/EMA
2025-03-04T20:59:08.8947350Z             device (torch.device, optional): if provided, the averaged model will be
2025-03-04T20:59:08.8947485Z                 stored on the :attr:`device`
2025-03-04T20:59:08.8947701Z             avg_fn (function, optional): the averaging function used to update
2025-03-04T20:59:08.8947928Z                 parameters; the function must take in the current value of the
2025-03-04T20:59:08.8948159Z                 :class:`AveragedModel` parameter, the current value of :attr:`model`
2025-03-04T20:59:08.8948379Z                 parameter, and the number of models already averaged; if None,
2025-03-04T20:59:08.8948548Z                 an equally weighted average is used (default: None)
2025-03-04T20:59:08.8948802Z             multi_avg_fn (function, optional): the averaging function used to update
2025-03-04T20:59:08.8949049Z                 parameters inplace; the function must take in the current values of the
2025-03-04T20:59:08.8949364Z                 :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2025-03-04T20:59:08.8949603Z                 parameters as a list, and the number of models already averaged; if None,
2025-03-04T20:59:08.8949783Z                 an equally weighted average is used (default: None)
2025-03-04T20:59:08.8950000Z             use_buffers (bool): if ``True``, it will compute running averages for
2025-03-04T20:59:08.8950248Z                 both the parameters and the buffers of the model. (default: ``False``)
2025-03-04T20:59:08.8950338Z     
2025-03-04T20:59:08.8950442Z         Example:
2025-03-04T20:59:08.8950582Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8950734Z             >>> loader, optimizer, model, loss_fn = ...
2025-03-04T20:59:08.8950916Z             >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2025-03-04T20:59:08.8951160Z             >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2025-03-04T20:59:08.8951286Z             >>>                                     T_max=300)
2025-03-04T20:59:08.8951404Z             >>> swa_start = 160
2025-03-04T20:59:08.8951583Z             >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2025-03-04T20:59:08.8951704Z             >>> for i in range(300):
2025-03-04T20:59:08.8951828Z             >>>      for input, target in loader:
2025-03-04T20:59:08.8951987Z             >>>          optimizer.zero_grad()
2025-03-04T20:59:08.8952134Z             >>>          loss_fn(model(input), target).backward()
2025-03-04T20:59:08.8952263Z             >>>          optimizer.step()
2025-03-04T20:59:08.8952372Z             >>>      if i > swa_start:
2025-03-04T20:59:08.8952524Z             >>>          swa_model.update_parameters(model)
2025-03-04T20:59:08.8952647Z             >>>          swa_scheduler.step()
2025-03-04T20:59:08.8952758Z             >>>      else:
2025-03-04T20:59:08.8952872Z             >>>          scheduler.step()
2025-03-04T20:59:08.8952964Z             >>>
2025-03-04T20:59:08.8953145Z             >>> # Update bn statistics for the swa_model at the end
2025-03-04T20:59:08.8953319Z             >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2025-03-04T20:59:08.8953423Z     
2025-03-04T20:59:08.8953736Z         You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2025-03-04T20:59:08.8953957Z         If no averaging function is provided, the default is to compute
2025-03-04T20:59:08.8954117Z         equally-weighted average of the weights (SWA).
2025-03-04T20:59:08.8954220Z     
2025-03-04T20:59:08.8954315Z         Example:
2025-03-04T20:59:08.8954467Z             >>> # xdoctest: +SKIP("undefined variables")
2025-03-04T20:59:08.8954714Z             >>> # Compute exponential moving averages of the weights and buffers
2025-03-04T20:59:08.8954902Z             >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2025-03-04T20:59:08.8955128Z             >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2025-03-04T20:59:08.8955230Z     
2025-03-04T20:59:08.8955336Z         .. note::
2025-03-04T20:59:08.8955576Z             When using SWA/EMA with models containing Batch Normalization you may
2025-03-04T20:59:08.8955792Z             need to update the activation statistics for Batch Normalization.
2025-03-04T20:59:08.8956056Z             This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2025-03-04T20:59:08.8956293Z             or by setting :attr:`use_buffers` to `True`. The first approach updates the
2025-03-04T20:59:08.8956561Z             statistics in a post-training step by passing data through the model. The
2025-03-04T20:59:08.8956809Z             second does it during the parameter update phase by averaging all buffers.
2025-03-04T20:59:08.8957078Z             Empirical evidence has shown that updating the statistics in normalization
2025-03-04T20:59:08.8957315Z             layers increases accuracy, but you may wish to empirically test which
2025-03-04T20:59:08.8957519Z             approach yields the best results in your problem.
2025-03-04T20:59:08.8957607Z     
2025-03-04T20:59:08.8957715Z         .. note::
2025-03-04T20:59:08.8957979Z             :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2025-03-04T20:59:08.8958079Z     
2025-03-04T20:59:08.8958175Z         .. note::
2025-03-04T20:59:08.8958397Z             When :meth:`update_parameters` is called for the first time (i.e.
2025-03-04T20:59:08.8958591Z             :attr:`n_averaged` is `0`) the parameters of `model` are copied
2025-03-04T20:59:08.8958814Z             to the parameters of :class:`AveragedModel`. For every subsequent
2025-03-04T20:59:08.8959011Z             call of :meth:`update_parameters` the function `avg_fn` is used
2025-03-04T20:59:08.8959143Z             to update the parameters.
2025-03-04T20:59:08.8959232Z     
2025-03-04T20:59:08.8959477Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-04T20:59:08.8959612Z             https://arxiv.org/abs/1803.05407
2025-03-04T20:59:08.8959870Z         .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2025-03-04T20:59:08.8960012Z             Average:
2025-03-04T20:59:08.8960147Z             https://arxiv.org/abs/1806.05594
2025-03-04T20:59:08.8960358Z         .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2025-03-04T20:59:08.8960519Z             https://arxiv.org/abs/1904.11943
2025-03-04T20:59:08.8960755Z         .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2025-03-04T20:59:08.8960874Z             Generalizes Well:
2025-03-04T20:59:08.8960999Z             https://arxiv.org/abs/2001.02312
2025-03-04T20:59:08.8961120Z         .. _Polyak averaging:
2025-03-04T20:59:08.8961301Z             https://paperswithcode.com/method/polyak-averaging
2025-03-04T20:59:08.8961407Z         
2025-03-04T20:59:08.8961666Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8961767Z     
2025-03-04T20:59:08.8961870Z       warnings.warn(msg)
2025-03-04T20:59:08.8961969Z     
2025-03-04T20:59:08.8962180Z --- Parse Warning: 105 / 116 ---
2025-03-04T20:59:08.8963059Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=369.
2025-03-04T20:59:08.8963332Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8963570Z     Anneals the learning rate in each parameter group to a fixed value.
2025-03-04T20:59:08.8963662Z     
2025-03-04T20:59:08.8963916Z         This learning rate scheduler is meant to be used with Stochastic Weight
2025-03-04T20:59:08.8964167Z         Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2025-03-04T20:59:08.8964272Z     
2025-03-04T20:59:08.8964369Z         Args:
2025-03-04T20:59:08.8964562Z             optimizer (torch.optim.Optimizer): wrapped optimizer
2025-03-04T20:59:08.8964780Z             swa_lrs (float or list): the learning rate value for all param groups
2025-03-04T20:59:08.8964935Z                 together or separately for each group.
2025-03-04T20:59:08.8965151Z             annealing_epochs (int): number of epochs in the annealing phase
2025-03-04T20:59:08.8965269Z                 (default: 10)
2025-03-04T20:59:08.8965495Z             annealing_strategy (str): "cos" or "linear"; specifies the annealing
2025-03-04T20:59:08.8965729Z                 strategy: "cos" for cosine annealing, "linear" for linear annealing
2025-03-04T20:59:08.8965835Z                 (default: "cos")
2025-03-04T20:59:08.8966036Z             last_epoch (int): the index of the last epoch (default: -1)
2025-03-04T20:59:08.8966126Z     
2025-03-04T20:59:08.8966324Z         The :class:`SWALR` scheduler can be used together with other
2025-03-04T20:59:08.8966551Z         schedulers to switch to a constant learning rate late in the training
2025-03-04T20:59:08.8966703Z         as in the example below.
2025-03-04T20:59:08.8966790Z     
2025-03-04T20:59:08.8966882Z         Example:
2025-03-04T20:59:08.8967040Z             >>> # xdoctest: +SKIP("Undefined variables")
2025-03-04T20:59:08.8967168Z             >>> loader, optimizer, model = ...
2025-03-04T20:59:08.8967302Z             >>> lr_lambda = lambda epoch: 0.9
2025-03-04T20:59:08.8967534Z             >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2025-03-04T20:59:08.8967661Z             >>>        lr_lambda=lr_lambda)
2025-03-04T20:59:08.8967841Z             >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2025-03-04T20:59:08.8968042Z             >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2025-03-04T20:59:08.8968149Z             >>> swa_start = 160
2025-03-04T20:59:08.8968267Z             >>> for i in range(300):
2025-03-04T20:59:08.8968392Z             >>>      for input, target in loader:
2025-03-04T20:59:08.8968528Z             >>>          optimizer.zero_grad()
2025-03-04T20:59:08.8968675Z             >>>          loss_fn(model(input), target).backward()
2025-03-04T20:59:08.8968831Z             >>>          optimizer.step()
2025-03-04T20:59:08.8968940Z             >>>      if i > swa_start:
2025-03-04T20:59:08.8969072Z             >>>          swa_scheduler.step()
2025-03-04T20:59:08.8969215Z             >>>      else:
2025-03-04T20:59:08.8969343Z             >>>          scheduler.step()
2025-03-04T20:59:08.8969432Z     
2025-03-04T20:59:08.8969682Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-03-04T20:59:08.8969807Z             https://arxiv.org/abs/1803.05407
2025-03-04T20:59:08.8969909Z         
2025-03-04T20:59:08.8970176Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.8970276Z     
2025-03-04T20:59:08.8970380Z       warnings.warn(msg)
2025-03-04T20:59:08.8970483Z     
2025-03-04T20:59:08.8970687Z --- Parse Warning: 106 / 116 ---
2025-03-04T20:59:08.8971611Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1263.
2025-03-04T20:59:08.8971887Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.8972061Z     Asserts that ``actual`` and ``expected`` are close.
2025-03-04T20:59:08.8972152Z     
2025-03-04T20:59:08.8972539Z         If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2025-03-04T20:59:08.8972628Z     
2025-03-04T20:59:08.8972741Z         .. math::
2025-03-04T20:59:08.8972831Z     
2025-03-04T20:59:08.8973246Z             \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2025-03-04T20:59:08.8973335Z     
2025-03-04T20:59:08.8973907Z         Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2025-03-04T20:59:08.8974130Z         only considered equal to each other if ``equal_nan`` is ``True``.
2025-03-04T20:59:08.8974234Z     
2025-03-04T20:59:08.8974447Z         In addition, they are only considered close if they have the same
2025-03-04T20:59:08.8974547Z     
2025-03-04T20:59:08.8974754Z         - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2025-03-04T20:59:08.8974910Z         - ``dtype`` (if ``check_dtype`` is ``True``),
2025-03-04T20:59:08.8975064Z         - ``layout`` (if ``check_layout`` is ``True``), and
2025-03-04T20:59:08.8975211Z         - stride (if ``check_stride`` is ``True``).
2025-03-04T20:59:08.8975297Z     
2025-03-04T20:59:08.8975618Z         If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2025-03-04T20:59:08.8975706Z     
2025-03-04T20:59:08.8976085Z         If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2025-03-04T20:59:08.8976547Z         checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2025-03-04T20:59:08.8976799Z         or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2025-03-04T20:59:08.8977197Z         are always checked for equality whereas the values are checked for closeness according to the definition above.
2025-03-04T20:59:08.8977299Z     
2025-03-04T20:59:08.8977590Z         If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2025-03-04T20:59:08.8978025Z         :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2025-03-04T20:59:08.8978135Z         definition above.
2025-03-04T20:59:08.8978234Z     
2025-03-04T20:59:08.8978549Z         ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2025-03-04T20:59:08.8978947Z         :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2025-03-04T20:59:08.8979367Z         have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2025-03-04T20:59:08.8979799Z         or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2025-03-04T20:59:08.8980041Z         their elements are considered close according to the above definition.
2025-03-04T20:59:08.8980141Z     
2025-03-04T20:59:08.8980240Z         .. note::
2025-03-04T20:59:08.8980343Z     
2025-03-04T20:59:08.8980686Z             Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2025-03-04T20:59:08.8981029Z             :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2025-03-04T20:59:08.8981319Z             Python scalars of different types can be checked, but require ``check_dtype=False``.
2025-03-04T20:59:08.8981420Z     
2025-03-04T20:59:08.8981512Z         Args:
2025-03-04T20:59:08.8981644Z             actual (Any): Actual input.
2025-03-04T20:59:08.8981769Z             expected (Any): Expected input.
2025-03-04T20:59:08.8982151Z             allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2025-03-04T20:59:08.8982320Z                 are allowed. Otherwise type equality is required.
2025-03-04T20:59:08.8982708Z             rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2025-03-04T20:59:08.8982984Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-04T20:59:08.8983412Z             atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2025-03-04T20:59:08.8983685Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-03-04T20:59:08.8983952Z             equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2025-03-04T20:59:08.8984261Z             check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2025-03-04T20:59:08.8984532Z                 :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2025-03-04T20:59:08.8984777Z                 :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2025-03-04T20:59:08.8985150Z             check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2025-03-04T20:59:08.8985506Z                 check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2025-03-04T20:59:08.8985687Z                 :func:`torch.promote_types`) before being compared.
2025-03-04T20:59:08.8986051Z             check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2025-03-04T20:59:08.8986433Z                 check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2025-03-04T20:59:08.8986538Z                 compared.
2025-03-04T20:59:08.8986924Z             check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2025-03-04T20:59:08.8987287Z             msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2025-03-04T20:59:08.8987665Z                 the comparison. Can also passed as callable in which case it will be called with the generated message and
2025-03-04T20:59:08.8987795Z                 should return the new message.
2025-03-04T20:59:08.8987896Z     
2025-03-04T20:59:08.8987991Z         Raises:
2025-03-04T20:59:08.8988248Z             ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2025-03-04T20:59:08.8988430Z             ValueError: If only ``rtol`` or ``atol`` is specified.
2025-03-04T20:59:08.8988779Z             AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2025-03-04T20:59:08.8989184Z             AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2025-03-04T20:59:08.8989334Z                 different types.
2025-03-04T20:59:08.8989709Z             AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2025-03-04T20:59:08.8990095Z             AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2025-03-04T20:59:08.8990425Z             AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2025-03-04T20:59:08.8990743Z             AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2025-03-04T20:59:08.8990869Z                 :attr:`~torch.Tensor.layout`.
2025-03-04T20:59:08.8991113Z             AssertionError: If only one of corresponding tensors is quantized.
2025-03-04T20:59:08.8991508Z             AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2025-03-04T20:59:08.8991824Z             AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2025-03-04T20:59:08.8991959Z                 :attr:`~torch.Tensor.device`.
2025-03-04T20:59:08.8992297Z             AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2025-03-04T20:59:08.8992700Z             AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2025-03-04T20:59:08.8993074Z             AssertionError: If the values of corresponding tensors are not close according to the definition above.
2025-03-04T20:59:08.8993174Z     
2025-03-04T20:59:08.8993542Z         The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2025-03-04T20:59:08.8993716Z         ``dtype``'s, the maximum of both tolerances is used.
2025-03-04T20:59:08.8993804Z     
2025-03-04T20:59:08.8993953Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8994094Z         | ``dtype``                 | ``rtol``   | ``atol`` |
2025-03-04T20:59:08.8994222Z         +===========================+============+==========+
2025-03-04T20:59:08.8994370Z         | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2025-03-04T20:59:08.8994516Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8994663Z         | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2025-03-04T20:59:08.8994810Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8994954Z         | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8995098Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8995280Z         | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2025-03-04T20:59:08.8995427Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8995574Z         | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2025-03-04T20:59:08.8995719Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8995864Z         | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8996008Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8996153Z         | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2025-03-04T20:59:08.8996284Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8996439Z         | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8996569Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8996729Z         | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8996861Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8997016Z         | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8997174Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8997326Z         | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8997482Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8997637Z         | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2025-03-04T20:59:08.8997771Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8997911Z         | other                     | ``0.0``    | ``0.0``  |
2025-03-04T20:59:08.8998044Z         +---------------------------+------------+----------+
2025-03-04T20:59:08.8998144Z     
2025-03-04T20:59:08.8998241Z         .. note::
2025-03-04T20:59:08.8998340Z     
2025-03-04T20:59:08.8998734Z             :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2025-03-04T20:59:08.8999110Z             to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2025-03-04T20:59:08.8999383Z             define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2025-03-04T20:59:08.8999483Z     
2025-03-04T20:59:08.8999594Z             >>> import functools
2025-03-04T20:59:08.8999875Z             >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2025-03-04T20:59:08.8999994Z             >>> assert_equal(1e-9, 1e-10)
2025-03-04T20:59:08.9000136Z             Traceback (most recent call last):
2025-03-04T20:59:08.9000230Z             ...
2025-03-04T20:59:08.9000406Z             AssertionError: Scalars are not equal!
2025-03-04T20:59:08.9000506Z             <BLANKLINE>
2025-03-04T20:59:08.9000638Z             Expected 1e-10 but got 1e-09.
2025-03-04T20:59:08.9000775Z             Absolute difference: 9.000000000000001e-10
2025-03-04T20:59:08.9000904Z             Relative difference: 9.0
2025-03-04T20:59:08.9000992Z     
2025-03-04T20:59:08.9001101Z         Examples:
2025-03-04T20:59:08.9001223Z             >>> # tensor to tensor comparison
2025-03-04T20:59:08.9001374Z             >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2025-03-04T20:59:08.9001517Z             >>> actual = torch.acos(torch.cos(expected))
2025-03-04T20:59:08.9001682Z             >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:08.9001772Z     
2025-03-04T20:59:08.9001906Z             >>> # scalar to scalar comparison
2025-03-04T20:59:08.9002011Z             >>> import math
2025-03-04T20:59:08.9002138Z             >>> expected = math.sqrt(2.0)
2025-03-04T20:59:08.9002259Z             >>> actual = 2.0 / math.sqrt(2.0)
2025-03-04T20:59:08.9002426Z             >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:08.9002517Z     
2025-03-04T20:59:08.9002662Z             >>> # numpy array to numpy array comparison
2025-03-04T20:59:08.9002800Z             >>> import numpy as np
2025-03-04T20:59:08.9002978Z             >>> expected = np.array([1e0, 1e-1, 1e-2])
2025-03-04T20:59:08.9003117Z             >>> actual = np.arccos(np.cos(expected))
2025-03-04T20:59:08.9003282Z             >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:08.9003370Z     
2025-03-04T20:59:08.9003514Z             >>> # sequence to sequence comparison
2025-03-04T20:59:08.9003624Z             >>> import numpy as np
2025-03-04T20:59:08.9003898Z             >>> # The types of the sequences do not have to match. They only have to have the same
2025-03-04T20:59:08.9004043Z             >>> # length and their elements have to match.
2025-03-04T20:59:08.9004224Z             >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2025-03-04T20:59:08.9004340Z             >>> actual = tuple(expected)
2025-03-04T20:59:08.9004508Z             >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:08.9004596Z     
2025-03-04T20:59:08.9004743Z             >>> # mapping to mapping comparison
2025-03-04T20:59:08.9004872Z             >>> from collections import OrderedDict
2025-03-04T20:59:08.9005009Z             >>> import numpy as np
2025-03-04T20:59:08.9005137Z             >>> foo = torch.tensor(1.0)
2025-03-04T20:59:08.9005236Z             >>> bar = 2.0
2025-03-04T20:59:08.9005382Z             >>> baz = np.array(3.0)
2025-03-04T20:59:08.9005647Z             >>> # The types and a possible ordering of mappings do not have to match. They only
2025-03-04T20:59:08.9005871Z             >>> # have to have the same set of keys and their elements have to match.
2025-03-04T20:59:08.9006082Z             >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2025-03-04T20:59:08.9006243Z             >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2025-03-04T20:59:08.9006398Z             >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:08.9006500Z     
2025-03-04T20:59:08.9006634Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-04T20:59:08.9006770Z             >>> actual = expected.clone()
2025-03-04T20:59:08.9006949Z             >>> # By default, directly related instances can be compared
2025-03-04T20:59:08.9007191Z             >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2025-03-04T20:59:08.9007396Z             >>> # This check can be made more strict with allow_subclasses=False
2025-03-04T20:59:08.9007534Z             >>> torch.testing.assert_close(
2025-03-04T20:59:08.9007744Z             ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2025-03-04T20:59:08.9007849Z             ... )
2025-03-04T20:59:08.9007978Z             Traceback (most recent call last):
2025-03-04T20:59:08.9008082Z             ...
2025-03-04T20:59:08.9008321Z             TypeError: No comparison pair was able to handle inputs of type
2025-03-04T20:59:08.9008560Z             <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2025-03-04T20:59:08.9008798Z             >>> # If the inputs are not directly related, they are never considered close
2025-03-04T20:59:08.9008995Z             >>> torch.testing.assert_close(actual.numpy(), expected)
2025-03-04T20:59:08.9009128Z             Traceback (most recent call last):
2025-03-04T20:59:08.9009237Z             ...
2025-03-04T20:59:08.9009539Z             TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2025-03-04T20:59:08.9009673Z             and <class 'torch.Tensor'>.
2025-03-04T20:59:08.9009947Z             >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2025-03-04T20:59:08.9010089Z             >>> # their type if check_dtype=False.
2025-03-04T20:59:08.9010265Z             >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2025-03-04T20:59:08.9010366Z     
2025-03-04T20:59:08.9010476Z             >>> # NaN != NaN by default.
2025-03-04T20:59:08.9010623Z             >>> expected = torch.tensor(float("Nan"))
2025-03-04T20:59:08.9010740Z             >>> actual = expected.clone()
2025-03-04T20:59:08.9010933Z             >>> torch.testing.assert_close(actual, expected)
2025-03-04T20:59:08.9011059Z             Traceback (most recent call last):
2025-03-04T20:59:08.9011164Z             ...
2025-03-04T20:59:08.9011296Z             AssertionError: Scalars are not close!
2025-03-04T20:59:08.9011406Z             <BLANKLINE>
2025-03-04T20:59:08.9011518Z             Expected nan but got nan.
2025-03-04T20:59:08.9011683Z             Absolute difference: nan (up to 1e-05 allowed)
2025-03-04T20:59:08.9011838Z             Relative difference: nan (up to 1.3e-06 allowed)
2025-03-04T20:59:08.9012059Z             >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2025-03-04T20:59:08.9012145Z     
2025-03-04T20:59:08.9012293Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-03-04T20:59:08.9012419Z             >>> actual = torch.tensor([1.0, 4.0, 5.0])
2025-03-04T20:59:08.9012583Z             >>> # The default error message can be overwritten.
2025-03-04T20:59:08.9012883Z             >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
2025-03-04T20:59:08.9013049Z             Traceback (most recent call last):
2025-03-04T20:59:08.9013138Z             ...
2025-03-04T20:59:08.9013312Z             AssertionError: Argh, the tensors are not close!
2025-03-04T20:59:08.9013567Z             >>> # If msg is a callable, it can be used to augment the generated message with
2025-03-04T20:59:08.9013688Z             >>> # extra information
2025-03-04T20:59:08.9013809Z             >>> torch.testing.assert_close(
2025-03-04T20:59:08.9014031Z             ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2025-03-04T20:59:08.9014118Z             ... )
2025-03-04T20:59:08.9014258Z             Traceback (most recent call last):
2025-03-04T20:59:08.9014347Z             ...
2025-03-04T20:59:08.9014471Z             AssertionError: Header
2025-03-04T20:59:08.9014569Z             <BLANKLINE>
2025-03-04T20:59:08.9014687Z             Tensor-likes are not close!
2025-03-04T20:59:08.9014794Z             <BLANKLINE>
2025-03-04T20:59:08.9014914Z             Mismatched elements: 2 / 3 (66.7%)
2025-03-04T20:59:08.9015162Z             Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2025-03-04T20:59:08.9015404Z             Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2025-03-04T20:59:08.9015515Z             <BLANKLINE>
2025-03-04T20:59:08.9015606Z             Footer
2025-03-04T20:59:08.9015705Z         
2025-03-04T20:59:08.9015966Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9016066Z     
2025-03-04T20:59:08.9016171Z       warnings.warn(msg)
2025-03-04T20:59:08.9016267Z     
2025-03-04T20:59:08.9016536Z --- Parse Warning: 107 / 116 ---
2025-03-04T20:59:08.9017476Z     /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=104.
2025-03-04T20:59:08.9017832Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9018002Z     Register a container-like type as pytree node.
2025-03-04T20:59:08.9018094Z     
2025-03-04T20:59:08.9018201Z         Args:
2025-03-04T20:59:08.9018401Z             cls (type): A Python type to treat as an internal pytree node.
2025-03-04T20:59:08.9018696Z             flatten_fn (callable): A function to be used during flattening, taking an instance of
2025-03-04T20:59:08.9018957Z                 ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2025-03-04T20:59:08.9019274Z                 recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2025-03-04T20:59:08.9019405Z                 passed to the ``unflatten_fn``.
2025-03-04T20:59:08.9019705Z             unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2025-03-04T20:59:08.9020011Z                 returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2025-03-04T20:59:08.9020193Z                 The function should return an instance of ``cls``.
2025-03-04T20:59:08.9020507Z             serialized_type_name (str, optional): A keyword argument used to specify the fully
2025-03-04T20:59:08.9020690Z                 qualified name used when serializing the tree spec.
2025-03-04T20:59:08.9021012Z             to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2025-03-04T20:59:08.9021310Z                 to convert the context of the pytree to a custom json dumpable representation. This is
2025-03-04T20:59:08.9021593Z                 used for json serialization, which is being used in :mod:`torch.export` right now.
2025-03-04T20:59:08.9021917Z             from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2025-03-04T20:59:08.9022192Z                 how to convert the custom json dumpable representation of the context back to the
2025-03-04T20:59:08.9022502Z                 original context. This is used for json deserialization, which is being used in
2025-03-04T20:59:08.9022639Z                 :mod:`torch.export` right now.
2025-03-04T20:59:08.9022722Z     
2025-03-04T20:59:08.9022823Z         Example::
2025-03-04T20:59:08.9022946Z     
2025-03-04T20:59:08.9023054Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9023218Z             >>> # Registry a Python type with lambda functions
2025-03-04T20:59:08.9023331Z             >>> register_pytree_node(
2025-03-04T20:59:08.9023437Z             ...     set,
2025-03-04T20:59:08.9023570Z             ...     lambda s: (sorted(s), None, None),
2025-03-04T20:59:08.9023714Z             ...     lambda children, _: set(children),
2025-03-04T20:59:08.9023804Z             ... )
2025-03-04T20:59:08.9023906Z         
2025-03-04T20:59:08.9024169Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9024270Z     
2025-03-04T20:59:08.9024373Z       warnings.warn(msg)
2025-03-04T20:59:08.9024473Z     
2025-03-04T20:59:08.9024680Z --- Parse Warning: 108 / 116 ---
2025-03-04T20:59:08.9025685Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1200.
2025-03-04T20:59:08.9025956Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9026053Z     
2025-03-04T20:59:08.9026274Z     Context passed to policy function during selective checkpointing.
2025-03-04T20:59:08.9026375Z     
2025-03-04T20:59:08.9026650Z     This class is used to pass relevant metadata to the policy function during
2025-03-04T20:59:08.9026938Z     selective checkpointing. The metadata includes whether the current invocation
2025-03-04T20:59:08.9027110Z     of the policy function is during recomputation or not.
2025-03-04T20:59:08.9027212Z     
2025-03-04T20:59:08.9027305Z     Example:
2025-03-04T20:59:08.9027431Z         >>> # xdoctest: +SKIP(stub)
2025-03-04T20:59:08.9027520Z         >>>
2025-03-04T20:59:08.9027671Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-04T20:59:08.9027788Z         >>>    print(ctx.is_recompute)
2025-03-04T20:59:08.9027891Z         >>>
2025-03-04T20:59:08.9028175Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-04T20:59:08.9028279Z         >>>
2025-03-04T20:59:08.9028430Z         >>> out = torch.utils.checkpoint.checkpoint(
2025-03-04T20:59:08.9028541Z         >>>     fn, x, y,
2025-03-04T20:59:08.9028653Z         >>>     use_reentrant=False,
2025-03-04T20:59:08.9028779Z         >>>     context_fn=context_fn,
2025-03-04T20:59:08.9028867Z         >>> )
2025-03-04T20:59:08.9028956Z     
2025-03-04T20:59:08.9029231Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9029348Z     
2025-03-04T20:59:08.9029465Z       warnings.warn(msg)
2025-03-04T20:59:08.9029556Z     
2025-03-04T20:59:08.9029761Z --- Parse Warning: 109 / 116 ---
2025-03-04T20:59:08.9030763Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1334.
2025-03-04T20:59:08.9031050Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9031137Z     
2025-03-04T20:59:08.9031395Z     Helper to avoid recomputing certain ops during activation checkpointing.
2025-03-04T20:59:08.9031488Z     
2025-03-04T20:59:08.9031726Z     Use this with `torch.utils.checkpoint.checkpoint` to control which
2025-03-04T20:59:08.9031897Z     operations are recomputed during the backward pass.
2025-03-04T20:59:08.9031999Z     
2025-03-04T20:59:08.9032090Z     Args:
2025-03-04T20:59:08.9032233Z         policy_fn_or_list (Callable or List):
2025-03-04T20:59:08.9032438Z           - If a policy function is provided, it should accept a
2025-03-04T20:59:08.9032701Z             :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2025-03-04T20:59:08.9032944Z             kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2025-03-04T20:59:08.9033208Z             indicating whether the execution of the op should be recomputed or not.
2025-03-04T20:59:08.9033419Z           - If a list of operations is provided, it is equivalent to a policy
2025-03-04T20:59:08.9033623Z             returning `CheckpointPolicy.MUST_SAVE` for the specified
2025-03-04T20:59:08.9033850Z             operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2025-03-04T20:59:08.9033958Z             operations.
2025-03-04T20:59:08.9034181Z         allow_cache_entry_mutation (bool, optional): By default, an error is
2025-03-04T20:59:08.9034416Z             raised if any tensors cached by selective activation checkpoint are
2025-03-04T20:59:08.9034635Z             mutated in order to ensure correctness. If set to `True`, this check
2025-03-04T20:59:08.9034747Z             is disabled.
2025-03-04T20:59:08.9034838Z     Returns:
2025-03-04T20:59:08.9034969Z         A tuple of two context managers.
2025-03-04T20:59:08.9035057Z     
2025-03-04T20:59:08.9035161Z     Example:
2025-03-04T20:59:08.9035278Z         >>> # xdoctest: +REQUIRES(LINUX)
2025-03-04T20:59:08.9035394Z         >>> import functools
2025-03-04T20:59:08.9035485Z         >>>
2025-03-04T20:59:08.9035635Z         >>> x = torch.rand(10, 10, requires_grad=True)
2025-03-04T20:59:08.9035767Z         >>> y = torch.rand(10, 10, requires_grad=True)
2025-03-04T20:59:08.9036198Z         >>>
2025-03-04T20:59:08.9036300Z         >>> ops_to_save = [
2025-03-04T20:59:08.9036433Z         >>>    torch.ops.aten.mm.default,
2025-03-04T20:59:08.9036524Z         >>> ]
2025-03-04T20:59:08.9036631Z         >>>
2025-03-04T20:59:08.9036766Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2025-03-04T20:59:08.9036889Z         >>>    if op in ops_to_save:
2025-03-04T20:59:08.9037029Z         >>>        return CheckpointPolicy.MUST_SAVE
2025-03-04T20:59:08.9037123Z         >>>    else:
2025-03-04T20:59:08.9037289Z         >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2025-03-04T20:59:08.9037384Z         >>>
2025-03-04T20:59:08.9037682Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-03-04T20:59:08.9037774Z         >>>
2025-03-04T20:59:08.9037890Z         >>> # or equivalently
2025-03-04T20:59:08.9038175Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2025-03-04T20:59:08.9038281Z         >>>
2025-03-04T20:59:08.9038381Z         >>> def fn(x, y):
2025-03-04T20:59:08.9038605Z         >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2025-03-04T20:59:08.9038697Z         >>>
2025-03-04T20:59:08.9038890Z         >>> out = torch.utils.checkpoint.checkpoint(
2025-03-04T20:59:08.9038991Z         >>>     fn, x, y,
2025-03-04T20:59:08.9039112Z         >>>     use_reentrant=False,
2025-03-04T20:59:08.9039227Z         >>>     context_fn=context_fn,
2025-03-04T20:59:08.9039326Z         >>> )
2025-03-04T20:59:08.9039416Z     
2025-03-04T20:59:08.9039691Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9039781Z     
2025-03-04T20:59:08.9039899Z       warnings.warn(msg)
2025-03-04T20:59:08.9039990Z     
2025-03-04T20:59:08.9040199Z --- Parse Warning: 110 / 116 ---
2025-03-04T20:59:08.9041123Z     /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=1064.
2025-03-04T20:59:08.9041405Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9041495Z     
2025-03-04T20:59:08.9041659Z     Create a :class:`setuptools.Extension` for C++.
2025-03-04T20:59:08.9041776Z     
2025-03-04T20:59:08.9042038Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-04T20:59:08.9042268Z     bare minimum (but often sufficient) arguments to build a C++ extension.
2025-03-04T20:59:08.9042433Z     
2025-03-04T20:59:08.9042650Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-04T20:59:08.9042818Z     constructor. Full list arguments can be found at
2025-03-04T20:59:08.9043157Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-04T20:59:08.9043254Z     
2025-03-04T20:59:08.9043353Z     .. warning::
2025-03-04T20:59:08.9043600Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-04T20:59:08.9043820Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-04T20:59:08.9044036Z         the user's responsibility in their library to not use APIs from
2025-03-04T20:59:08.9044275Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-04T20:59:08.9044511Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-04T20:59:08.9044736Z         example, to give access to custom ops from python, the library should
2025-03-04T20:59:08.9044886Z         register the ops through the dispatcher.
2025-03-04T20:59:08.9044973Z     
2025-03-04T20:59:08.9045215Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-04T20:59:08.9045428Z         as a compile flag when py_limited_api is specified as an option for
2025-03-04T20:59:08.9045678Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-04T20:59:08.9045902Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-04T20:59:08.9046135Z         safety, and sanity in order to encourage best practices. To target a
2025-03-04T20:59:08.9046359Z         different version, set min_supported_cpython to the hexcode of the
2025-03-04T20:59:08.9046485Z         CPython version of choice.
2025-03-04T20:59:08.9046573Z     
2025-03-04T20:59:08.9046676Z     Example:
2025-03-04T20:59:08.9046783Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9046945Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:08.9047076Z         >>> from setuptools import setup
2025-03-04T20:59:08.9047306Z         >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2025-03-04T20:59:08.9047415Z         >>> setup(
2025-03-04T20:59:08.9047523Z         ...     name='extension',
2025-03-04T20:59:08.9047634Z         ...     ext_modules=[
2025-03-04T20:59:08.9047740Z         ...         CppExtension(
2025-03-04T20:59:08.9047861Z         ...             name='extension',
2025-03-04T20:59:08.9047990Z         ...             sources=['extension.cpp'],
2025-03-04T20:59:08.9048123Z         ...             extra_compile_args=['-g'],
2025-03-04T20:59:08.9048311Z         ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2025-03-04T20:59:08.9048412Z         ...     ],
2025-03-04T20:59:08.9048512Z         ...     cmdclass={
2025-03-04T20:59:08.9048646Z         ...         'build_ext': BuildExtension
2025-03-04T20:59:08.9048736Z         ...     })
2025-03-04T20:59:08.9048833Z     
2025-03-04T20:59:08.9049099Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9049194Z     
2025-03-04T20:59:08.9049300Z       warnings.warn(msg)
2025-03-04T20:59:08.9049400Z     
2025-03-04T20:59:08.9049598Z --- Parse Warning: 111 / 116 ---
2025-03-04T20:59:08.9050530Z     /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=1134.
2025-03-04T20:59:08.9050804Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9050906Z     
2025-03-04T20:59:08.9051077Z     Create a :class:`setuptools.Extension` for CUDA/C++.
2025-03-04T20:59:08.9051207Z     
2025-03-04T20:59:08.9051461Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-04T20:59:08.9051704Z     bare minimum (but often sufficient) arguments to build a CUDA/C++
2025-03-04T20:59:08.9051945Z     extension. This includes the CUDA include path, library path and runtime
2025-03-04T20:59:08.9052046Z     library.
2025-03-04T20:59:08.9052133Z     
2025-03-04T20:59:08.9052359Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-04T20:59:08.9052517Z     constructor. Full list arguments can be found at
2025-03-04T20:59:08.9052871Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-03-04T20:59:08.9052961Z     
2025-03-04T20:59:08.9053071Z     .. warning::
2025-03-04T20:59:08.9053308Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-04T20:59:08.9053535Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-04T20:59:08.9053741Z         the user's responsibility in their library to not use APIs from
2025-03-04T20:59:08.9053990Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-04T20:59:08.9054215Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-04T20:59:08.9054449Z         example, to give access to custom ops from python, the library should
2025-03-04T20:59:08.9054585Z         register the ops through the dispatcher.
2025-03-04T20:59:08.9054681Z     
2025-03-04T20:59:08.9054913Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-03-04T20:59:08.9055164Z         as a compile flag when py_limited_api is specified as an option for
2025-03-04T20:59:08.9055381Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-03-04T20:59:08.9055613Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-03-04T20:59:08.9055833Z         safety, and sanity in order to encourage best practices. To target a
2025-03-04T20:59:08.9056067Z         different version, set min_supported_cpython to the hexcode of the
2025-03-04T20:59:08.9056178Z         CPython version of choice.
2025-03-04T20:59:08.9056276Z     
2025-03-04T20:59:08.9056369Z     Example:
2025-03-04T20:59:08.9056485Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9056644Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:08.9056772Z         >>> from setuptools import setup
2025-03-04T20:59:08.9057000Z         >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2025-03-04T20:59:08.9057092Z         >>> setup(
2025-03-04T20:59:08.9057218Z         ...     name='cuda_extension',
2025-03-04T20:59:08.9057320Z         ...     ext_modules=[
2025-03-04T20:59:08.9057438Z         ...         CUDAExtension(
2025-03-04T20:59:08.9057591Z         ...                 name='cuda_extension',
2025-03-04T20:59:08.9057852Z         ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-04T20:59:08.9057999Z         ...                 extra_compile_args={'cxx': ['-g'],
2025-03-04T20:59:08.9058137Z         ...                                     'nvcc': ['-O2']},
2025-03-04T20:59:08.9058302Z         ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2025-03-04T20:59:08.9058405Z         ...     ],
2025-03-04T20:59:08.9058503Z         ...     cmdclass={
2025-03-04T20:59:08.9058640Z         ...         'build_ext': BuildExtension
2025-03-04T20:59:08.9058730Z         ...     })
2025-03-04T20:59:08.9058830Z     
2025-03-04T20:59:08.9058939Z     Compute capabilities:
2025-03-04T20:59:08.9059043Z     
2025-03-04T20:59:08.9059355Z     By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-04T20:59:08.9059668Z     building process of the extension, plus PTX. If down the road a new card is installed the
2025-03-04T20:59:08.9059972Z     extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2025-03-04T20:59:08.9060328Z     newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
2025-03-04T20:59:08.9060666Z     will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2025-03-04T20:59:08.9060806Z     support (see below for details on PTX).
2025-03-04T20:59:08.9060894Z     
2025-03-04T20:59:08.9061229Z     You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2025-03-04T20:59:08.9061355Z     CCs you want the extension to support:
2025-03-04T20:59:08.9061454Z     
2025-03-04T20:59:08.9061654Z     ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2025-03-04T20:59:08.9061906Z     ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2025-03-04T20:59:08.9061996Z     
2025-03-04T20:59:08.9062339Z     The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2025-03-04T20:59:08.9062668Z     CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2025-03-04T20:59:08.9062992Z     the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2025-03-04T20:59:08.9063296Z     CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2025-03-04T20:59:08.9063641Z     provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2025-03-04T20:59:08.9063922Z     those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2025-03-04T20:59:08.9064289Z     off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2025-03-04T20:59:08.9064613Z     "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2025-03-04T20:59:08.9064727Z     "8.0 8.6" would be better.
2025-03-04T20:59:08.9064815Z     
2025-03-04T20:59:08.9065131Z     Note that while it's possible to include all supported archs, the more archs get included the
2025-03-04T20:59:08.9065434Z     slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-04T20:59:08.9065530Z     
2025-03-04T20:59:08.9065871Z     Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2025-03-04T20:59:08.9066099Z     To workaround the issue, move python binding logic to pure C++ file.
2025-03-04T20:59:08.9066189Z     
2025-03-04T20:59:08.9066294Z     Example use:
2025-03-04T20:59:08.9066407Z         #include <ATen/ATen.h>
2025-03-04T20:59:08.9066585Z         at::Tensor SigmoidAlphaBlendForwardCuda(....)
2025-03-04T20:59:08.9066674Z     
2025-03-04T20:59:08.9066784Z     Instead of:
2025-03-04T20:59:08.9066903Z         #include <torch/extension.h>
2025-03-04T20:59:08.9067085Z         torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2025-03-04T20:59:08.9067201Z     
2025-03-04T20:59:08.9067499Z     Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2025-03-04T20:59:08.9068025Z     Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2025-03-04T20:59:08.9068129Z     
2025-03-04T20:59:08.9068250Z     Relocatable device code linking:
2025-03-04T20:59:08.9068353Z     
2025-03-04T20:59:08.9068643Z     If you want to reference device symbols across compilation units (across object files),
2025-03-04T20:59:08.9068924Z     the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2025-03-04T20:59:08.9069299Z     An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2025-03-04T20:59:08.9069655Z     `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2025-03-04T20:59:08.9069995Z     Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2025-03-04T20:59:08.9070228Z     helps reduce the protentional perf degradation of `-rdc`.
2025-03-04T20:59:08.9070408Z     Note that it needs to be used at both steps to be useful.
2025-03-04T20:59:08.9070512Z     
2025-03-04T20:59:08.9070918Z     If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2025-03-04T20:59:08.9071118Z     There is also a case where `-dlink` is used without `-rdc`:
2025-03-04T20:59:08.9071387Z     when an extension is linked against a static lib containing rdc-compiled objects
2025-03-04T20:59:08.9071625Z     like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2025-03-04T20:59:08.9071718Z     
2025-03-04T20:59:08.9071942Z     Note: Ninja is required to build a CUDA Extension with RDC linking.
2025-03-04T20:59:08.9072032Z     
2025-03-04T20:59:08.9072143Z     Example:
2025-03-04T20:59:08.9072252Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9072424Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:08.9072533Z         >>> CUDAExtension(
2025-03-04T20:59:08.9072661Z         ...        name='cuda_extension',
2025-03-04T20:59:08.9072829Z         ...        sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-04T20:59:08.9072945Z         ...        dlink=True,
2025-03-04T20:59:08.9073076Z         ...        dlink_libraries=["dlink_lib"],
2025-03-04T20:59:08.9073222Z         ...        extra_compile_args={'cxx': ['-g'],
2025-03-04T20:59:08.9073354Z         ...                            'nvcc': ['-O2', '-rdc=true']})
2025-03-04T20:59:08.9073457Z     
2025-03-04T20:59:08.9074020Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9074115Z     
2025-03-04T20:59:08.9074238Z       warnings.warn(msg)
2025-03-04T20:59:08.9074325Z     
2025-03-04T20:59:08.9074570Z --- Parse Warning: 112 / 116 ---
2025-03-04T20:59:08.9075493Z     /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=1325.
2025-03-04T20:59:08.9075778Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9075865Z     
2025-03-04T20:59:08.9076052Z     Creates a :class:`setuptools.Extension` for SYCL/C++.
2025-03-04T20:59:08.9076140Z     
2025-03-04T20:59:08.9076404Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-03-04T20:59:08.9076612Z     bare minimum (but often sufficient) arguments to build a SYCL/C++
2025-03-04T20:59:08.9076718Z     extension.
2025-03-04T20:59:08.9076806Z     
2025-03-04T20:59:08.9077036Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-03-04T20:59:08.9077135Z     constructor.
2025-03-04T20:59:08.9077239Z     
2025-03-04T20:59:08.9077336Z     .. note::
2025-03-04T20:59:08.9077636Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-03-04T20:59:08.9077855Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-03-04T20:59:08.9078079Z         the user's responsibility in their library to not use APIs from
2025-03-04T20:59:08.9078323Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-03-04T20:59:08.9078562Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-03-04T20:59:08.9078788Z         example, to give access to custom ops from python, the library should
2025-03-04T20:59:08.9078939Z         register the ops through the dispatcher.
2025-03-04T20:59:08.9079028Z     
2025-03-04T20:59:08.9079136Z     Example:
2025-03-04T20:59:08.9079245Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9079417Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:08.9079651Z         >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
2025-03-04T20:59:08.9079760Z         >>> setup(
2025-03-04T20:59:08.9079874Z         ...     name='xpu_extension',
2025-03-04T20:59:08.9080023Z         ...     ext_modules=[
2025-03-04T20:59:08.9080129Z         ...     SyclExtension(
2025-03-04T20:59:08.9080264Z         ...                 name='xpu_extension',
2025-03-04T20:59:08.9080475Z         ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
2025-03-04T20:59:08.9080680Z         ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
2025-03-04T20:59:08.9080773Z         ...     ],
2025-03-04T20:59:08.9080886Z         ...     cmdclass={
2025-03-04T20:59:08.9081012Z         ...         'build_ext': BuildExtension
2025-03-04T20:59:08.9081114Z         ...     })
2025-03-04T20:59:08.9081206Z     
2025-03-04T20:59:08.9081527Z     By default the extension will be compiled to run on all archs of the cards visible during the
2025-03-04T20:59:08.9081790Z     building process of the extension. If down the road a new card is installed the
2025-03-04T20:59:08.9082068Z     extension may need to be recompiled. You can override the default behavior using
2025-03-04T20:59:08.9082393Z     `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
2025-03-04T20:59:08.9082499Z     to support:
2025-03-04T20:59:08.9082587Z     
2025-03-04T20:59:08.9082806Z     ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
2025-03-04T20:59:08.9082895Z     
2025-03-04T20:59:08.9083215Z     Note that while it's possible to include all supported archs, the more archs get included the
2025-03-04T20:59:08.9083519Z     slower the building process will be, as it will build a separate kernel image for each arch.
2025-03-04T20:59:08.9083621Z     
2025-03-04T20:59:08.9083800Z     Note: Ninja is required to build SyclExtension.
2025-03-04T20:59:08.9083890Z     
2025-03-04T20:59:08.9084163Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9084255Z     
2025-03-04T20:59:08.9084373Z       warnings.warn(msg)
2025-03-04T20:59:08.9084462Z     
2025-03-04T20:59:08.9084682Z --- Parse Warning: 113 / 116 ---
2025-03-04T20:59:08.9085557Z     /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=1494.
2025-03-04T20:59:08.9085842Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9085929Z     
2025-03-04T20:59:08.9086095Z     Load a PyTorch C++ extension just-in-time (JIT).
2025-03-04T20:59:08.9086183Z     
2025-03-04T20:59:08.9086418Z     To load an extension, a Ninja build file is emitted, which is used to
2025-03-04T20:59:08.9086637Z     compile the given sources into a dynamic library. This library is
2025-03-04T20:59:08.9086882Z     subsequently loaded into the current Python process as a module and
2025-03-04T20:59:08.9087049Z     returned from this function, ready for use.
2025-03-04T20:59:08.9087151Z     
2025-03-04T20:59:08.9087368Z     By default, the directory to which the build file is emitted and the
2025-03-04T20:59:08.9087625Z     resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2025-03-04T20:59:08.9087840Z     ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2025-03-04T20:59:08.9088072Z     the name of the extension. This location can be overridden in two ways.
2025-03-04T20:59:08.9088289Z     First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2025-03-04T20:59:08.9088531Z     replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2025-03-04T20:59:08.9088758Z     into subfolders of this directory. Second, if the ``build_directory``
2025-03-04T20:59:08.9089015Z     argument to this function is supplied, it overrides the entire path, i.e.
2025-03-04T20:59:08.9089190Z     the library will be compiled into that folder directly.
2025-03-04T20:59:08.9089294Z     
2025-03-04T20:59:08.9089516Z     To compile the sources, the default system compiler (``c++``) is used,
2025-03-04T20:59:08.9089806Z     which can be overridden by setting the ``CXX`` environment variable. To pass
2025-03-04T20:59:08.9090041Z     additional arguments to the compilation process, ``extra_cflags`` or
2025-03-04T20:59:08.9090307Z     ``extra_ldflags`` can be provided. For example, to compile your extension
2025-03-04T20:59:08.9090531Z     with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2025-03-04T20:59:08.9090708Z     ``extra_cflags`` to pass further include directories.
2025-03-04T20:59:08.9090796Z     
2025-03-04T20:59:08.9091052Z     CUDA support with mixed compilation is provided. Simply pass CUDA source
2025-03-04T20:59:08.9091249Z     files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2025-03-04T20:59:08.9091509Z     detected and compiled with nvcc rather than the C++ compiler. This includes
2025-03-04T20:59:08.9091729Z     passing the CUDA lib64 directory as a library directory, and linking
2025-03-04T20:59:08.9091903Z     ``cudart``. You can pass additional flags to nvcc via
2025-03-04T20:59:08.9092122Z     ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2025-03-04T20:59:08.9092378Z     heuristics for finding the CUDA install directory are used, which usually
2025-03-04T20:59:08.9092599Z     work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2025-03-04T20:59:08.9092708Z     safest option.
2025-03-04T20:59:08.9092795Z     
2025-03-04T20:59:08.9093049Z     SYCL support with mixed compilation is provided. Simply pass SYCL source
2025-03-04T20:59:08.9093258Z     files (``.sycl``) along with other sources. Such files will be detected
2025-03-04T20:59:08.9093520Z     and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
2025-03-04T20:59:08.9093736Z     than the C++ compiler. You can pass additional flags to SYCL compiler
2025-03-04T20:59:08.9093949Z     via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
2025-03-04T20:59:08.9094166Z     SYCL compiler is expected to be found via system PATH environment
2025-03-04T20:59:08.9094273Z     variable.
2025-03-04T20:59:08.9094362Z     
2025-03-04T20:59:08.9094464Z     Args:
2025-03-04T20:59:08.9094684Z         name: The name of the extension to build. This MUST be the same as the
2025-03-04T20:59:08.9094817Z             name of the pybind11 module!
2025-03-04T20:59:08.9095027Z         sources: A list of relative or absolute paths to C++ source files.
2025-03-04T20:59:08.9095275Z         extra_cflags: optional list of compiler flags to forward to the build.
2025-03-04T20:59:08.9095507Z         extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2025-03-04T20:59:08.9095641Z             when building CUDA sources.
2025-03-04T20:59:08.9095871Z         extra_sycl_cflags: optional list of compiler flags to forward to SYCL
2025-03-04T20:59:08.9096013Z             compiler when building SYCL sources.
2025-03-04T20:59:08.9096268Z         extra_ldflags: optional list of linker flags to forward to the build.
2025-03-04T20:59:08.9096512Z         extra_include_paths: optional list of include directories to forward
2025-03-04T20:59:08.9096617Z             to the build.
2025-03-04T20:59:08.9096819Z         build_directory: optional path to use as build workspace.
2025-03-04T20:59:08.9097010Z         verbose: If ``True``, turns on verbose logging of load steps.
2025-03-04T20:59:08.9097252Z         with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-04T20:59:08.9097420Z             the build. If set to ``None`` (default), this value is
2025-03-04T20:59:08.9097644Z             automatically determined based on the existence of ``.cu`` or
2025-03-04T20:59:08.9097897Z             ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2025-03-04T20:59:08.9098028Z             and libraries to be included.
2025-03-04T20:59:08.9098261Z         with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-04T20:59:08.9098445Z             the build. If set to ``None`` (default), this value is
2025-03-04T20:59:08.9098709Z             automatically determined based on the existence of ``.sycl`` in
2025-03-04T20:59:08.9098891Z             ``sources``. Set it to `True`` to force SYCL headers and
2025-03-04T20:59:08.9099006Z             libraries to be included.
2025-03-04T20:59:08.9099261Z         is_python_module: If ``True`` (default), imports the produced shared
2025-03-04T20:59:08.9099458Z             library as a Python module. If ``False``, behavior depends on
2025-03-04T20:59:08.9099578Z             ``is_standalone``.
2025-03-04T20:59:08.9099795Z         is_standalone: If ``False`` (default) loads the constructed extension
2025-03-04T20:59:08.9100017Z             into the process as a plain dynamic library. If ``True``, build a
2025-03-04T20:59:08.9100132Z             standalone executable.
2025-03-04T20:59:08.9100236Z     
2025-03-04T20:59:08.9100332Z     Returns:
2025-03-04T20:59:08.9100470Z         If ``is_python_module`` is ``True``:
2025-03-04T20:59:08.9100660Z             Returns the loaded PyTorch extension as a Python module.
2025-03-04T20:59:08.9100764Z     
2025-03-04T20:59:08.9100979Z         If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2025-03-04T20:59:08.9101215Z             Returns nothing. (The shared library is loaded into the process as
2025-03-04T20:59:08.9101320Z             a side effect.)
2025-03-04T20:59:08.9101422Z     
2025-03-04T20:59:08.9101540Z         If ``is_standalone`` is ``True``.
2025-03-04T20:59:08.9101765Z             Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2025-03-04T20:59:08.9101952Z             added to the PATH environment variable as a side effect.)
2025-03-04T20:59:08.9102078Z     
2025-03-04T20:59:08.9102174Z     Example:
2025-03-04T20:59:08.9102284Z         >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9102448Z         >>> from torch.utils.cpp_extension import load
2025-03-04T20:59:08.9102553Z         >>> module = load(
2025-03-04T20:59:08.9102676Z         ...     name='extension',
2025-03-04T20:59:08.9102848Z         ...     sources=['extension.cpp', 'extension_kernel.cu'],
2025-03-04T20:59:08.9102975Z         ...     extra_cflags=['-O2'],
2025-03-04T20:59:08.9103079Z         ...     verbose=True)
2025-03-04T20:59:08.9103182Z     
2025-03-04T20:59:08.9103451Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9103555Z     
2025-03-04T20:59:08.9103662Z       warnings.warn(msg)
2025-03-04T20:59:08.9103763Z     
2025-03-04T20:59:08.9103977Z --- Parse Warning: 114 / 116 ---
2025-03-04T20:59:08.9104893Z     /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=1803.
2025-03-04T20:59:08.9105166Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9105297Z     
2025-03-04T20:59:08.9105512Z     Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2025-03-04T20:59:08.9105617Z     
2025-03-04T20:59:08.9105858Z     This function behaves exactly like :func:`load`, but takes its sources as
2025-03-04T20:59:08.9106109Z     strings rather than filenames. These strings are stored to files in the
2025-03-04T20:59:08.9106331Z     build directory, after which the behavior of :func:`load_inline` is
2025-03-04T20:59:08.9106455Z     identical to :func:`load`.
2025-03-04T20:59:08.9106543Z     
2025-03-04T20:59:08.9106647Z     See `the
2025-03-04T20:59:08.9106985Z     tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2025-03-04T20:59:08.9107135Z     for good examples of using this function.
2025-03-04T20:59:08.9107223Z     
2025-03-04T20:59:08.9107475Z     Sources may omit two required parts of a typical non-inline C++ extension:
2025-03-04T20:59:08.9107726Z     the necessary header includes, as well as the (pybind11) binding code. More
2025-03-04T20:59:08.9107986Z     precisely, strings passed to ``cpp_sources`` are first concatenated into a
2025-03-04T20:59:08.9108210Z     single ``.cpp`` file. This file is then prepended with ``#include
2025-03-04T20:59:08.9108330Z     <torch/extension.h>``.
2025-03-04T20:59:08.9108422Z     
2025-03-04T20:59:08.9108692Z     Furthermore, if the ``functions`` argument is supplied, bindings will be
2025-03-04T20:59:08.9108940Z     automatically generated for each function specified. ``functions`` can
2025-03-04T20:59:08.9109186Z     either be a list of function names, or a dictionary mapping from function
2025-03-04T20:59:08.9109418Z     names to docstrings. If a list is given, the name of each function is used
2025-03-04T20:59:08.9109533Z     as its docstring.
2025-03-04T20:59:08.9109620Z     
2025-03-04T20:59:08.9109855Z     The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2025-03-04T20:59:08.9110041Z     file and  prepended with ``torch/types.h``, ``cuda.h`` and
2025-03-04T20:59:08.9110277Z     ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2025-03-04T20:59:08.9110512Z     separately, but ultimately linked into a single library. Note that no
2025-03-04T20:59:08.9110764Z     bindings are generated for functions in ``cuda_sources`` per se. To bind
2025-03-04T20:59:08.9110993Z     to a CUDA kernel, you must create a C++ function that calls it, and either
2025-03-04T20:59:08.9111231Z     declare or define this C++ function in one of the ``cpp_sources`` (and
2025-03-04T20:59:08.9111352Z     include its name in ``functions``).
2025-03-04T20:59:08.9111451Z     
2025-03-04T20:59:08.9111681Z     The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
2025-03-04T20:59:08.9111946Z     file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
2025-03-04T20:59:08.9112152Z     The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
2025-03-04T20:59:08.9112391Z     linked into a single library. Note that no bindings are generated for
2025-03-04T20:59:08.9112616Z     functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
2025-03-04T20:59:08.9112856Z     create a C++ function that calls it, and either declare or define this
2025-03-04T20:59:08.9113054Z     C++ function in one of the ``cpp_sources`` (and include its name
2025-03-04T20:59:08.9113172Z     in ``functions``).
2025-03-04T20:59:08.9113260Z     
2025-03-04T20:59:08.9113464Z     See :func:`load` for a description of arguments omitted below.
2025-03-04T20:59:08.9113554Z     
2025-03-04T20:59:08.9113657Z     Args:
2025-03-04T20:59:08.9113884Z         cpp_sources: A string, or list of strings, containing C++ source code.
2025-03-04T20:59:08.9114131Z         cuda_sources: A string, or list of strings, containing CUDA source code.
2025-03-04T20:59:08.9114363Z         sycl_sources: A string, or list of strings, containing SYCL source code.
2025-03-04T20:59:08.9114586Z         functions: A list of function names for which to generate function
2025-03-04T20:59:08.9114838Z             bindings. If a dictionary is given, it should map function names to
2025-03-04T20:59:08.9115044Z             docstrings (which are otherwise just the function names).
2025-03-04T20:59:08.9115274Z         with_cuda: Determines whether CUDA headers and libraries are added to
2025-03-04T20:59:08.9115458Z             the build. If set to ``None`` (default), this value is
2025-03-04T20:59:08.9115671Z             automatically determined based on whether ``cuda_sources`` is
2025-03-04T20:59:08.9115848Z             provided. Set it to ``True`` to force CUDA headers
2025-03-04T20:59:08.9115969Z             and libraries to be included.
2025-03-04T20:59:08.9116215Z         with_sycl: Determines whether SYCL headers and libraries are added to
2025-03-04T20:59:08.9116382Z             the build. If set to ``None`` (default), this value is
2025-03-04T20:59:08.9116608Z             automatically determined based on whether ``sycl_sources`` is
2025-03-04T20:59:08.9116774Z             provided. Set it to ``True`` to force SYCL headers
2025-03-04T20:59:08.9116905Z             and libraries to be included.
2025-03-04T20:59:08.9117150Z         with_pytorch_error_handling: Determines whether pytorch error and
2025-03-04T20:59:08.9117374Z             warning macros are handled by pytorch instead of pybind. To do
2025-03-04T20:59:08.9117618Z             this, each function ``foo`` is called via an intermediary ``_safe_foo``
2025-03-04T20:59:08.9117841Z             function. This redirection might cause issues in obscure cases
2025-03-04T20:59:08.9118035Z             of cpp. This flag should be set to ``False`` when this redirect
2025-03-04T20:59:08.9118149Z             causes issues.
2025-03-04T20:59:08.9118236Z     
2025-03-04T20:59:08.9118332Z     Example:
2025-03-04T20:59:08.9118505Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-03-04T20:59:08.9118674Z         >>> from torch.utils.cpp_extension import load_inline
2025-03-04T20:59:08.9118786Z         >>> source = """
2025-03-04T20:59:08.9118944Z         at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2025-03-04T20:59:08.9119063Z           return x.sin() + y.sin();
2025-03-04T20:59:08.9119154Z         }
2025-03-04T20:59:08.9119253Z         """
2025-03-04T20:59:08.9119399Z         >>> module = load_inline(name='inline_extension',
2025-03-04T20:59:08.9119541Z         ...                      cpp_sources=[source],
2025-03-04T20:59:08.9119668Z         ...                      functions=['sin_add'])
2025-03-04T20:59:08.9119767Z     
2025-03-04T20:59:08.9119862Z     .. note::
2025-03-04T20:59:08.9120120Z         Since load_inline will just-in-time compile the source code, please ensure
2025-03-04T20:59:08.9120381Z         that you have the right toolchains installed in the runtime. For example,
2025-03-04T20:59:08.9120622Z         when loading C++, make sure a C++ compiler is available. If you're loading
2025-03-04T20:59:08.9120876Z         a CUDA extension, you will need to additionally install the corresponding CUDA
2025-03-04T20:59:08.9121141Z         toolkit (nvcc and any other dependencies your code has). Compiling toolchains
2025-03-04T20:59:08.9121390Z         are not included when you install torch and must be additionally installed.
2025-03-04T20:59:08.9121492Z     
2025-03-04T20:59:08.9121753Z         During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
2025-03-04T20:59:08.9121994Z         the extension. This may use up too many resources on some systems. One
2025-03-04T20:59:08.9122223Z         can control the number of workers by setting the `MAX_JOBS` environment
2025-03-04T20:59:08.9122361Z         variable to a non-negative number.
2025-03-04T20:59:08.9122449Z     
2025-03-04T20:59:08.9122724Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9122813Z     
2025-03-04T20:59:08.9122932Z       warnings.warn(msg)
2025-03-04T20:59:08.9123019Z     
2025-03-04T20:59:08.9123241Z --- Parse Warning: 115 / 116 ---
2025-03-04T20:59:08.9124252Z     /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.
2025-03-04T20:59:08.9124541Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9124630Z     
2025-03-04T20:59:08.9124943Z     This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2025-03-04T20:59:08.9125030Z     
2025-03-04T20:59:08.9125347Z     This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2025-03-04T20:59:08.9125607Z     for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2025-03-04T20:59:08.9125860Z     server like load. It can emulate multiple calling threads to a single module
2025-03-04T20:59:08.9126109Z     provided. In the future we plan to enhance this component to support inter and
2025-03-04T20:59:08.9126378Z     intra-op parallelism as well as multiple models running in a single process.
2025-03-04T20:59:08.9126492Z     
2025-03-04T20:59:08.9126766Z     Please note that even though nn.Module is supported, it might incur an overhead
2025-03-04T20:59:08.9126998Z     from the need to hold GIL every time we execute Python code or pass around
2025-03-04T20:59:08.9127282Z     inputs as Python objects. As soon as you have a ScriptModule version of your
2025-03-04T20:59:08.9127530Z     model for inference deployment it is better to switch to using it in this
2025-03-04T20:59:08.9127638Z     benchmark.
2025-03-04T20:59:08.9127727Z     
2025-03-04T20:59:08.9127838Z     Example::
2025-03-04T20:59:08.9127927Z     
2025-03-04T20:59:08.9128074Z         >>> # xdoctest: +SKIP("undefined vars")
2025-03-04T20:59:08.9128227Z         >>> from torch.utils import ThroughputBenchmark
2025-03-04T20:59:08.9128380Z         >>> bench = ThroughputBenchmark(my_module)
2025-03-04T20:59:08.9128548Z         >>> # Pre-populate benchmark's data set with the inputs
2025-03-04T20:59:08.9128677Z         >>> for input in inputs:
2025-03-04T20:59:08.9128908Z         ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2025-03-04T20:59:08.9129060Z         ...     bench.add_input(input[0], x2=input[1])
2025-03-04T20:59:08.9129271Z         >>> # Inputs supplied above are randomly used during the execution
2025-03-04T20:59:08.9129394Z         >>> stats = bench.benchmark(
2025-03-04T20:59:08.9129508Z         ...     num_calling_threads=4,
2025-03-04T20:59:08.9129630Z         ...     num_warmup_iters = 100,
2025-03-04T20:59:08.9129735Z         ...     num_iters = 1000,
2025-03-04T20:59:08.9129836Z         ... )
2025-03-04T20:59:08.9130050Z         >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2025-03-04T20:59:08.9130246Z         >>> print("Number of iterations: {}".format(stats.num_iters))
2025-03-04T20:59:08.9130334Z     
2025-03-04T20:59:08.9130606Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9130695Z     
2025-03-04T20:59:08.9130809Z       warnings.warn(msg)
2025-03-04T20:59:08.9130897Z     
2025-03-04T20:59:08.9131098Z --- Parse Warning: 116 / 116 ---
2025-03-04T20:59:08.9132065Z     /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.
2025-03-04T20:59:08.9132358Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-03-04T20:59:08.9132572Z     Sampler that restricts data loading to a subset of the dataset.
2025-03-04T20:59:08.9132678Z     
2025-03-04T20:59:08.9132826Z         It is especially useful in conjunction with
2025-03-04T20:59:08.9133098Z         :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2025-03-04T20:59:08.9133368Z         process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2025-03-04T20:59:08.9133660Z         :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2025-03-04T20:59:08.9133802Z         original dataset that is exclusive to it.
2025-03-04T20:59:08.9133906Z     
2025-03-04T20:59:08.9134003Z         .. note::
2025-03-04T20:59:08.9134253Z             Dataset is assumed to be of constant size and that any instance of it always
2025-03-04T20:59:08.9134416Z             returns the same elements in the same order.
2025-03-04T20:59:08.9134506Z     
2025-03-04T20:59:08.9134615Z         Args:
2025-03-04T20:59:08.9134744Z             dataset: Dataset used for sampling.
2025-03-04T20:59:08.9134987Z             num_replicas (int, optional): Number of processes participating in
2025-03-04T20:59:08.9135251Z                 distributed training. By default, :attr:`world_size` is retrieved from the
2025-03-04T20:59:08.9135391Z                 current distributed group.
2025-03-04T20:59:08.9135642Z             rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2025-03-04T20:59:08.9135871Z                 By default, :attr:`rank` is retrieved from the current distributed
2025-03-04T20:59:08.9135994Z                 group.
2025-03-04T20:59:08.9136245Z             shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2025-03-04T20:59:08.9136372Z                 indices.
2025-03-04T20:59:08.9136590Z             seed (int, optional): random seed used to shuffle the sampler if
2025-03-04T20:59:08.9136799Z                 :attr:`shuffle=True`. This number should be identical across all
2025-03-04T20:59:08.9136989Z                 processes in the distributed group. Default: ``0``.
2025-03-04T20:59:08.9137217Z             drop_last (bool, optional): if ``True``, then the sampler will drop the
2025-03-04T20:59:08.9137436Z                 tail of the data to make it evenly divisible across the number of
2025-03-04T20:59:08.9137642Z                 replicas. If ``False``, the sampler will add extra indices to make
2025-03-04T20:59:08.9137959Z                 the data evenly divisible across the replicas. Default: ``False``.
2025-03-04T20:59:08.9138055Z     
2025-03-04T20:59:08.9138166Z         .. warning::
2025-03-04T20:59:08.9138367Z             In distributed mode, calling the :meth:`set_epoch` method at
2025-03-04T20:59:08.9138648Z             the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2025-03-04T20:59:08.9138914Z             is necessary to make shuffling work properly across multiple epochs. Otherwise,
2025-03-04T20:59:08.9139060Z             the same ordering will be always used.
2025-03-04T20:59:08.9139150Z     
2025-03-04T20:59:08.9139259Z         Example::
2025-03-04T20:59:08.9139348Z     
2025-03-04T20:59:08.9139497Z             >>> # xdoctest: +SKIP
2025-03-04T20:59:08.9139728Z             >>> sampler = DistributedSampler(dataset) if is_distributed else None
2025-03-04T20:59:08.9139970Z             >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2025-03-04T20:59:08.9140149Z             ...                     sampler=sampler)
2025-03-04T20:59:08.9140310Z             >>> for epoch in range(start_epoch, n_epochs):
2025-03-04T20:59:08.9140423Z             ...     if is_distributed:
2025-03-04T20:59:08.9140559Z             ...         sampler.set_epoch(epoch)
2025-03-04T20:59:08.9140664Z             ...     train(loader)
2025-03-04T20:59:08.9140770Z         
2025-03-04T20:59:08.9141035Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-03-04T20:59:08.9141136Z     
2025-03-04T20:59:08.9141244Z       warnings.warn(msg)
2025-03-04T20:59:08.9141346Z     
2025-03-04T20:59:08.9141485Z 
2025-03-04T20:59:08.9141685Z === Found 10 run-time warnings ===
2025-03-04T20:59:08.9141877Z --- Runtime Warning: 1 / 10 ---
2025-03-04T20:59:08.9142161Z example = <DocTest(torch._tensor Tensor.refine_names:0 ln 1347) at 0x7f6376b23f00>
2025-03-04T20:59:08.9143514Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1365: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1938.)
2025-03-04T20:59:08.9143697Z       return super().refine_names(names)
2025-03-04T20:59:08.9143787Z     
2025-03-04T20:59:08.9143988Z --- Runtime Warning: 2 / 10 ---
2025-03-04T20:59:08.9144309Z example = <DocTest(torch.library Library._impl_with_aoti_compile:0 ln 251) at 0x7f6376915f20>
2025-03-04T20:59:08.9144962Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:288: UserWarning: Warning only once for all operators,  other operators may also be overridden.
2025-03-04T20:59:08.9145285Z       Overriding a previously registered kernel for the same operator and the same dispatch key
2025-03-04T20:59:08.9145506Z       operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor
2025-03-04T20:59:08.9145814Z         registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6
2025-03-04T20:59:08.9145960Z       dispatch key: CPU
2025-03-04T20:59:08.9146400Z       previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079
2025-03-04T20:59:08.9147001Z            new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:161.)
2025-03-04T20:59:08.9147172Z       impl_fn(self.ns, name.split("::")[-1], dispatch_key)
2025-03-04T20:59:08.9147274Z     
2025-03-04T20:59:08.9147470Z --- Runtime Warning: 3 / 10 ---
2025-03-04T20:59:08.9147728Z example = <DocTest(torch.nested as_nested_tensor:0 ln 61) at 0x7f6376a389e0>
2025-03-04T20:59:08.9149568Z     /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.)
2025-03-04T20:59:08.9149841Z       return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
2025-03-04T20:59:08.9149930Z     
2025-03-04T20:59:08.9150129Z --- Runtime Warning: 4 / 10 ---
2025-03-04T20:59:08.9150396Z example = <DocTest(torch.sparse as_sparse_gradcheck:0 ln 561) at 0x7f6376a38f30>
2025-03-04T20:59:08.9152068Z     <doctest:/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0>:1: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
2025-03-04T20:59:08.9152172Z     
2025-03-04T20:59:08.9152360Z --- Runtime Warning: 5 / 10 ---
2025-03-04T20:59:08.9152692Z example = <DocTest(torch._functorch.eager_transforms linearize:0 ln 1704) at 0x7f637427de10>
2025-03-04T20:59:08.9154199Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:264: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2025-03-04T20:59:08.9154387Z       new_node = root_const_gm.graph.get_attr(in_node.target)
2025-03-04T20:59:08.9154477Z     
2025-03-04T20:59:08.9154677Z --- Runtime Warning: 6 / 10 ---
2025-03-04T20:59:08.9154975Z example = <DocTest(torch.nn.modules.transformer Transformer:0 ln 88) at 0x7f6376a399d0>
2025-03-04T20:59:08.9156107Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:382: 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)
2025-03-04T20:59:08.9156213Z       warnings.warn(
2025-03-04T20:59:08.9156314Z     
2025-03-04T20:59:08.9156505Z --- Runtime Warning: 7 / 10 ---
2025-03-04T20:59:08.9156857Z example = <DocTest(torch.nn.modules.transformer TransformerEncoder:0 ln 326) at 0x7f63768fb240>
2025-03-04T20:59:08.9157941Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:382: 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)
2025-03-04T20:59:08.9158058Z       warnings.warn(
2025-03-04T20:59:08.9158147Z     
2025-03-04T20:59:08.9158348Z --- Runtime Warning: 8 / 10 ---
2025-03-04T20:59:08.9158636Z example = <DocTest(torch.nn.utils.weight_norm weight_norm:0 ln 133) at 0x7f63768d9260>
2025-03-04T20:59:08.9159496Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
2025-03-04T20:59:08.9159651Z       WeightNorm.apply(module, name, dim)
2025-03-04T20:59:08.9159754Z     
2025-03-04T20:59:08.9159942Z --- Runtime Warning: 9 / 10 ---
2025-03-04T20:59:08.9160269Z example = <DocTest(torch.nn.utils.weight_norm remove_weight_norm:0 ln 155) at 0x7f63768d9370>
2025-03-04T20:59:08.9161079Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
2025-03-04T20:59:08.9161220Z       WeightNorm.apply(module, name, dim)
2025-03-04T20:59:08.9161308Z     
2025-03-04T20:59:08.9161510Z --- Runtime Warning: 10 / 10 ---
2025-03-04T20:59:08.9161795Z example = <DocTest(torch.utils._pytree register_dataclass:0 ln 268) at 0x7f63768db350>
2025-03-04T20:59:08.9162654Z     /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py:453: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
2025-03-04T20:59:08.9162779Z       _register_pytree_node(
2025-03-04T20:59:08.9162868Z     
2025-03-04T20:59:08.9163173Z === 342 passed, 367 skipped, 126 warnings in 13.30 seconds ===
2025-03-04T20:59:08.9163393Z Running test_autoload_disable 1/1 ... [2025-03-04 20:59:08.745920]
2025-03-04T20:59:11.3839561Z running install
2025-03-04T20:59:11.3840830Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:59:11.3841663Z !!
2025-03-04T20:59:11.3841806Z 
2025-03-04T20:59:11.3841961Z         ********************************************************************************
2025-03-04T20:59:11.3842374Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:59:11.3842806Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:59:11.3843201Z         standards-based tools.
2025-03-04T20:59:11.3843399Z 
2025-03-04T20:59:11.3843729Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:59:11.3844282Z         ********************************************************************************
2025-03-04T20:59:11.3844533Z 
2025-03-04T20:59:11.3844635Z !!
2025-03-04T20:59:11.3844876Z   self.initialize_options()
2025-03-04T20:59:11.3973911Z running build
2025-03-04T20:59:11.3974179Z running build_py
2025-03-04T20:59:11.4050627Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:59:11.4053332Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:59:11.4056900Z running build_ext
2025-03-04T20:59:11.4831984Z building 'torch_test_cpp_extension.cpp' extension
2025-03-04T20:59:11.4833229Z creating build/temp.linux-x86_64-cpython-313
2025-03-04T20:59:11.4838547Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:59:12.5063692Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2025-03-04T20:59:12.5064773Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2025-03-04T20:59:12.5065910Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9,
2025-03-04T20:59:12.5066484Z                  from extension.cpp:1:
2025-03-04T20:59:12.5068149Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2025-03-04T20:59:12.5069426Z extension.cpp:45:53:   required from here
2025-03-04T20:59:12.5071956Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2025-03-04T20:59:12.5075568Z  1539 | class class_ : public detail::generic_type {
2025-03-04T20:59:12.5076036Z       |       ^~~~~~
2025-03-04T20:59:12.5077817Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2025-03-04T20:59:12.5079253Z extension.cpp:45:53:   required from here
2025-03-04T20:59:12.5082659Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2025-03-04T20:59:12.5085404Z  1599 |             with_internals([&](internals &internals) {
2025-03-04T20:59:12.5085811Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:12.5086357Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2025-03-04T20:59:12.5086959Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:12.5087430Z  1601 |                                                       : internals.registered_types_cpp;
2025-03-04T20:59:12.5087864Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:12.5088309Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2025-03-04T20:59:12.5088740Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:12.5089229Z  1603 |                     = instances[std::type_index(typeid(type))];
2025-03-04T20:59:12.5089652Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:12.5090004Z  1604 |             });
2025-03-04T20:59:12.5090279Z       |             ~               
2025-03-04T20:59:12.5093533Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:59:12.9169232Z building 'torch_test_cpp_extension.maia' extension
2025-03-04T20:59:12.9173158Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:59:13.9056496Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:59:14.2894036Z building 'torch_test_cpp_extension.rng' extension
2025-03-04T20:59:14.2898404Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:59:15.4974720Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:59:15.4975692Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:59:15.4976510Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:59:15.4977617Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:59:15.4978383Z                  from rng_extension.cpp:6:
2025-03-04T20:59:15.4979226Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1155: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:15.4980058Z  1155 | # pragma unroll
2025-03-04T20:59:15.4980346Z       | 
2025-03-04T20:59:15.4980905Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1195,
2025-03-04T20:59:15.4981971Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:59:15.4982970Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:59:15.4983789Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:59:15.4984718Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:59:15.4985516Z                  from rng_extension.cpp:6:
2025-03-04T20:59:15.4986384Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:15.4987181Z    59 | #pragma unroll
2025-03-04T20:59:15.4987448Z       | 
2025-03-04T20:59:15.4988144Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:15.4988933Z    72 | #pragma unroll
2025-03-04T20:59:15.4989197Z       | 
2025-03-04T20:59:15.4989882Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:15.4990674Z    87 | #pragma unroll
2025-03-04T20:59:15.4990941Z       | 
2025-03-04T20:59:15.4991506Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1196,
2025-03-04T20:59:15.4992437Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:59:15.4993267Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:59:15.4994077Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:59:15.4995027Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:59:15.4995783Z                  from rng_extension.cpp:6:
2025-03-04T20:59:15.4996616Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:15.4997440Z   153 | #pragma unroll
2025-03-04T20:59:15.4997709Z       | 
2025-03-04T20:59:15.5000855Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:59:15.9129991Z running install_lib
2025-03-04T20:59:15.9209690Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:59:15.9298975Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:59:15.9387977Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:59:15.9484073Z running install_egg_info
2025-03-04T20:59:15.9659770Z running egg_info
2025-03-04T20:59:15.9734596Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-04T20:59:15.9738085Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-04T20:59:15.9740060Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-04T20:59:15.9742048Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-04T20:59:15.9816535Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:59:15.9825176Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:59:15.9827311Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-04T20:59:15.9828831Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-04T20:59:15.9834300Z running install_scripts
2025-03-04T20:59:19.3115176Z 
2025-03-04T20:59:19.3115651Z Running tests...
2025-03-04T20:59:19.3116078Z ----------------------------------------------------------------------
2025-03-04T20:59:19.4497241Z .
2025-03-04T20:59:19.4497648Z ----------------------------------------------------------------------
2025-03-04T20:59:19.4498146Z Ran 1 test in 0.138s
2025-03-04T20:59:19.4498345Z 
2025-03-04T20:59:19.4498451Z OK
2025-03-04T20:59:19.4498569Z 
2025-03-04T20:59:19.4498686Z Generating XML reports...
2025-03-04T20:59:20.2006299Z Running test_autoload_enable 1/1 ... [2025-03-04 20:59:20.200296]
2025-03-04T20:59:22.8181648Z running install
2025-03-04T20:59:22.8182870Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-03-04T20:59:22.8183692Z !!
2025-03-04T20:59:22.8183813Z 
2025-03-04T20:59:22.8183958Z         ********************************************************************************
2025-03-04T20:59:22.8184371Z         Please avoid running ``setup.py`` directly.
2025-03-04T20:59:22.8184995Z         Instead, use pypa/build, pypa/installer or other
2025-03-04T20:59:22.8185396Z         standards-based tools.
2025-03-04T20:59:22.8185607Z 
2025-03-04T20:59:22.8185923Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-03-04T20:59:22.8186483Z         ********************************************************************************
2025-03-04T20:59:22.8186753Z 
2025-03-04T20:59:22.8186843Z !!
2025-03-04T20:59:22.8187085Z   self.initialize_options()
2025-03-04T20:59:22.8317165Z running build
2025-03-04T20:59:22.8317470Z running build_py
2025-03-04T20:59:22.8395531Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:59:22.8398102Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension
2025-03-04T20:59:22.8401796Z running build_ext
2025-03-04T20:59:22.9181217Z building 'torch_test_cpp_extension.cpp' extension
2025-03-04T20:59:22.9182127Z creating build/temp.linux-x86_64-cpython-313
2025-03-04T20:59:22.9187317Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:59:23.9251620Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2025-03-04T20:59:23.9253314Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2025-03-04T20:59:23.9254849Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9,
2025-03-04T20:59:23.9255796Z                  from extension.cpp:1:
2025-03-04T20:59:23.9257409Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2025-03-04T20:59:23.9258570Z extension.cpp:45:53:   required from here
2025-03-04T20:59:23.9260135Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2025-03-04T20:59:23.9261632Z  1539 | class class_ : public detail::generic_type {
2025-03-04T20:59:23.9262068Z       |       ^~~~~~
2025-03-04T20:59:23.9263791Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2025-03-04T20:59:23.9265201Z extension.cpp:45:53:   required from here
2025-03-04T20:59:23.9268501Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2025-03-04T20:59:23.9271232Z  1599 |             with_internals([&](internals &internals) {
2025-03-04T20:59:23.9271728Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:23.9272272Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2025-03-04T20:59:23.9272876Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:23.9273351Z  1601 |                                                       : internals.registered_types_cpp;
2025-03-04T20:59:23.9273961Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:23.9274412Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2025-03-04T20:59:23.9274840Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:23.9275260Z  1603 |                     = instances[std::type_index(typeid(type))];
2025-03-04T20:59:23.9275665Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2025-03-04T20:59:23.9276022Z  1604 |             });
2025-03-04T20:59:23.9276292Z       |             ~               
2025-03-04T20:59:23.9279529Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:59:24.3326664Z building 'torch_test_cpp_extension.maia' extension
2025-03-04T20:59:24.3332033Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:59:25.3128375Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:59:25.6944755Z building 'torch_test_cpp_extension.rng' extension
2025-03-04T20:59:25.6949670Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2025-03-04T20:59:26.8474911Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:59:26.8475873Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:59:26.8476701Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:59:26.8477651Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:59:26.8478346Z                  from rng_extension.cpp:6:
2025-03-04T20:59:26.8479192Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1155: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:26.8480020Z  1155 | # pragma unroll
2025-03-04T20:59:26.8480295Z       | 
2025-03-04T20:59:26.8481015Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1195,
2025-03-04T20:59:26.8481973Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:59:26.8482875Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:59:26.8483790Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:59:26.8484743Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:59:26.8485441Z                  from rng_extension.cpp:6:
2025-03-04T20:59:26.8486259Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:26.8487057Z    59 | #pragma unroll
2025-03-04T20:59:26.8487327Z       | 
2025-03-04T20:59:26.8488024Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:26.8488899Z    72 | #pragma unroll
2025-03-04T20:59:26.8489170Z       | 
2025-03-04T20:59:26.8489935Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:26.8490731Z    87 | #pragma unroll
2025-03-04T20:59:26.8491000Z       | 
2025-03-04T20:59:26.8491564Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1196,
2025-03-04T20:59:26.8492497Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2025-03-04T20:59:26.8493336Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7,
2025-03-04T20:59:26.8494163Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2025-03-04T20:59:26.8495101Z                  from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2025-03-04T20:59:26.8495796Z                  from rng_extension.cpp:6:
2025-03-04T20:59:26.8496629Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2025-03-04T20:59:26.8497442Z   153 | #pragma unroll
2025-03-04T20:59:26.8497716Z       | 
2025-03-04T20:59:26.8501020Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so
2025-03-04T20:59:27.2529286Z running install_lib
2025-03-04T20:59:27.2610462Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:59:27.2708982Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:59:27.2806530Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension
2025-03-04T20:59:27.2909520Z running install_egg_info
2025-03-04T20:59:27.3086490Z running egg_info
2025-03-04T20:59:27.3155946Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2025-03-04T20:59:27.3159042Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2025-03-04T20:59:27.3160941Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2025-03-04T20:59:27.3162975Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2025-03-04T20:59:27.3239087Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:59:27.3247204Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2025-03-04T20:59:27.3248814Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it)
2025-03-04T20:59:27.3250364Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info
2025-03-04T20:59:27.3256316Z running install_scripts
2025-03-04T20:59:30.6421049Z 
2025-03-04T20:59:30.6421512Z Running tests...
2025-03-04T20:59:30.6421927Z ----------------------------------------------------------------------
2025-03-04T20:59:30.7772619Z .
2025-03-04T20:59:30.7773225Z ----------------------------------------------------------------------
2025-03-04T20:59:30.7773792Z Ran 1 test in 0.135s
2025-03-04T20:59:30.7773983Z 
2025-03-04T20:59:30.7774076Z OK
2025-03-04T20:59:30.7774211Z 
2025-03-04T20:59:30.7774329Z Generating XML reports...
2025-03-04T20:59:31.4932387Z Running dynamo/test_graph_break_messages 1/1 ... [2025-03-04 20:59:31.492893]
2025-03-04T20:59:31.4932961Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:31.4935315Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_break_messages.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'] ... [2025-03-04 20:59:31.493297]
2025-03-04T20:59:34.7660546Z 
2025-03-04T20:59:34.7661861Z dynamo/test_graph_break_messages 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_break_messages_1.1_1f34828315ffbbe3_.log
2025-03-04T20:59:34.7662734Z 
2025-03-04T20:59:34.7664110Z Running dynamo/test_export 1/1 ... [2025-03-04 20:59:34.766213]
2025-03-04T20:59:34.7664807Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:34.7668356Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:59:34.766564]
2025-03-04T20:59:38.0868942Z 
2025-03-04T20:59:38.0870175Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_7b93e16fc1bf0e48_.log
2025-03-04T20:59:38.0870886Z 
2025-03-04T20:59:38.0872494Z Running dynamo/test_repros 1/1 ... [2025-03-04 20:59:38.087035]
2025-03-04T20:59:38.0873159Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:38.0876280Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_repros.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:59:38.087351]
2025-03-04T20:59:41.4567666Z 
2025-03-04T20:59:41.4568864Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_bbceb1007cb10153_.log
2025-03-04T20:59:41.4569575Z 
2025-03-04T20:59:41.4571163Z Running dynamo/test_decorators 1/1 ... [2025-03-04 20:59:41.456933]
2025-03-04T20:59:41.4571838Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:41.4575475Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_decorators.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'] ... [2025-03-04 20:59:41.457258]
2025-03-04T20:59:44.7091005Z 
2025-03-04T20:59:44.7091991Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_74fbba70258671bc_.log
2025-03-04T20:59:44.7092712Z 
2025-03-04T20:59:44.7094201Z Running dynamo/test_optimizers 1/1 ... [2025-03-04 20:59:44.709255]
2025-03-04T20:59:44.7094671Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:44.7098280Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:59:44.709586]
2025-03-04T20:59:47.9697920Z 
2025-03-04T20:59:47.9699086Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_864f46f19a8386ca_.log
2025-03-04T20:59:47.9700217Z 
2025-03-04T20:59:47.9701296Z Running dynamo/test_minifier 1/1 ... [2025-03-04 20:59:47.969937]
2025-03-04T20:59:47.9701804Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:47.9704982Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:59:47.970261]
2025-03-04T20:59:51.2356779Z 
2025-03-04T20:59:51.2357813Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_b41724883f970a49_.log
2025-03-04T20:59:51.2358570Z 
2025-03-04T20:59:51.2360090Z Running dynamo/test_backends 1/1 ... [2025-03-04 20:59:51.235838]
2025-03-04T20:59:51.2360631Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:51.2363913Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backends.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:59:51.236156]
2025-03-04T20:59:55.0682651Z 
2025-03-04T20:59:55.0683597Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_2cf2f33105bd837e_.log
2025-03-04T20:59:55.0684300Z 
2025-03-04T20:59:55.0686287Z Running dynamo/test_aot_autograd 1/1 ... [2025-03-04 20:59:55.068459]
2025-03-04T20:59:55.0687024Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:55.0690461Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 20:59:55.068794]
2025-03-04T20:59:58.3501400Z 
2025-03-04T20:59:58.3502410Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_2a8d4b4c9a3818a5_.log
2025-03-04T20:59:58.3503127Z 
2025-03-04T20:59:58.3504206Z Running dynamo/test_functions 1/1 ... [2025-03-04 20:59:58.350274]
2025-03-04T20:59:58.3504722Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T20:59:58.3508191Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_functions.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'] ... [2025-03-04 20:59:58.350591]
2025-03-04T21:00:02.2145735Z 
2025-03-04T21:00:02.2146739Z dynamo/test_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_functions_1.1_0c8c20fa6289bc21_.log
2025-03-04T21:00:02.2147803Z 
2025-03-04T21:00:02.2148757Z Running dynamo/test_skip_non_tensor 1/1 ... [2025-03-04 21:00:02.214717]
2025-03-04T21:00:02.2149356Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:02.2152598Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:02.215035]
2025-03-04T21:00:05.5482959Z 
2025-03-04T21:00:05.5483968Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_f17869b3afac6ac8_.log
2025-03-04T21:00:05.5484752Z 
2025-03-04T21:00:05.5486313Z Running dynamo/test_pre_dispatch 1/1 ... [2025-03-04 21:00:05.548464]
2025-03-04T21:00:05.5486788Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:05.5490224Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_pre_dispatch.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'] ... [2025-03-04 21:00:05.548785]
2025-03-04T21:00:08.8149596Z 
2025-03-04T21:00:08.8151054Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_e567909049f33bf0_.log
2025-03-04T21:00:08.8151864Z 
2025-03-04T21:00:08.8153015Z Running dynamo/test_python_autograd 1/1 ... [2025-03-04 21:00:08.815132]
2025-03-04T21:00:08.8153499Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:08.8156831Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:08.815457]
2025-03-04T21:00:12.1256914Z 
2025-03-04T21:00:12.1258249Z dynamo/test_python_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_python_autograd_1.1_3138967e066acfde_.log
2025-03-04T21:00:12.1259116Z 
2025-03-04T21:00:12.1260316Z Running dynamo/test_exceptions 1/1 ... [2025-03-04 21:00:12.125841]
2025-03-04T21:00:12.1261036Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:12.1264433Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:12.126184]
2025-03-04T21:00:15.3936414Z 
2025-03-04T21:00:15.3937977Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_5af685361ff1b9ca_.log
2025-03-04T21:00:15.3938780Z 
2025-03-04T21:00:15.3940088Z Running dynamo/test_hooks 1/1 ... [2025-03-04 21:00:15.393814]
2025-03-04T21:00:15.3940738Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:15.3944050Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_hooks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:15.394135]
2025-03-04T21:00:18.6875389Z 
2025-03-04T21:00:18.6876339Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_9f96a76c48d863cb_.log
2025-03-04T21:00:18.6876999Z 
2025-03-04T21:00:18.6878980Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2025-03-04 21:00:18.687698]
2025-03-04T21:00:18.6879575Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:18.6882783Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.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'] ... [2025-03-04 21:00:18.688022]
2025-03-04T21:00:21.9754099Z 
2025-03-04T21:00:21.9755417Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_a2a0a91c41dc7018_.log
2025-03-04T21:00:21.9756319Z 
2025-03-04T21:00:21.9757953Z Running dynamo/test_base_output 1/1 ... [2025-03-04 21:00:21.975569]
2025-03-04T21:00:21.9758515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:21.9761333Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:21.975890]
2025-03-04T21:00:25.2961171Z 
2025-03-04T21:00:25.2962594Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_d623c79781ed36b7_.log
2025-03-04T21:00:25.2963977Z 
2025-03-04T21:00:25.2964247Z Running dynamo/test_reconstruct 1/1 ... [2025-03-04 21:00:25.296193]
2025-03-04T21:00:25.2964728Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:25.2968388Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_reconstruct.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'] ... [2025-03-04 21:00:25.296567]
2025-03-04T21:00:28.5572993Z 
2025-03-04T21:00:28.5574160Z dynamo/test_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reconstruct_1.1_43cab3740971c523_.log
2025-03-04T21:00:28.5574889Z 
2025-03-04T21:00:28.5577003Z Running dynamo/test_view 1/1 ... [2025-03-04 21:00:28.557477]
2025-03-04T21:00:28.5577473Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:28.5580380Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_view.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'] ... [2025-03-04 21:00:28.557822]
2025-03-04T21:00:31.8436335Z 
2025-03-04T21:00:31.8437295Z dynamo/test_view 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_view_1.1_de0891d33f83d8d3_.log
2025-03-04T21:00:31.8437954Z 
2025-03-04T21:00:31.8439277Z Running dynamo/test_trace_rules 1/1 ... [2025-03-04 21:00:31.843773]
2025-03-04T21:00:31.8439799Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:31.8443416Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:31.844089]
2025-03-04T21:00:35.1169862Z 
2025-03-04T21:00:35.1170817Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_e2e95bb59405ec18_.log
2025-03-04T21:00:35.1171569Z 
2025-03-04T21:00:35.1172987Z Running dynamo/test_compile 1/1 ... [2025-03-04 21:00:35.117129]
2025-03-04T21:00:35.1173468Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:35.1177038Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compile.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:35.117471]
2025-03-04T21:00:38.3834872Z 
2025-03-04T21:00:38.3836513Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_2ab09fd9fb1ec736_.log
2025-03-04T21:00:38.3837596Z 
2025-03-04T21:00:38.3839244Z Running dynamo/test_deviceguard 1/1 ... [2025-03-04 21:00:38.383728]
2025-03-04T21:00:38.3839957Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:38.3843916Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:38.384148]
2025-03-04T21:00:41.6730470Z 
2025-03-04T21:00:41.6731515Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_44e05eb4640e09a6_.log
2025-03-04T21:00:41.6732240Z 
2025-03-04T21:00:41.6733832Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-03-04 21:00:41.673203]
2025-03-04T21:00:41.6734418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:41.6737630Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backward_higher_order_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:41.673517]
2025-03-04T21:00:44.9502811Z 
2025-03-04T21:00:44.9504167Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_eefcfbc4e4f72a04_.log
2025-03-04T21:00:44.9505056Z 
2025-03-04T21:00:44.9506579Z Running dynamo/test_base_hop 1/1 ... [2025-03-04 21:00:44.950448]
2025-03-04T21:00:44.9507060Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:44.9509976Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_hop.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'] ... [2025-03-04 21:00:44.950769]
2025-03-04T21:00:48.7003128Z 
2025-03-04T21:00:48.7004088Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_c8ed99c35676a224_.log
2025-03-04T21:00:48.7004811Z 
2025-03-04T21:00:48.7006439Z Running dynamo/test_bytecode_utils 1/1 ... [2025-03-04 21:00:48.700459]
2025-03-04T21:00:48.7006989Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:48.7010261Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:48.700788]
2025-03-04T21:00:51.9819004Z 
2025-03-04T21:00:51.9820198Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_c714123fd4ae37a4_.log
2025-03-04T21:00:51.9820960Z 
2025-03-04T21:00:51.9822441Z Running dynamo/test_aot_autograd_cache 1/1 ... [2025-03-04 21:00:51.982069]
2025-03-04T21:00:51.9823015Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:51.9826396Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:51.982390]
2025-03-04T21:00:55.7650970Z 
2025-03-04T21:00:55.7652034Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_cc55ba2dd5589533_.log
2025-03-04T21:00:55.7652829Z 
2025-03-04T21:00:55.7654269Z Running dynamo/test_input_attr_tracking 1/1 ... [2025-03-04 21:00:55.765257]
2025-03-04T21:00:55.7654814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:55.7658187Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_input_attr_tracking.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'] ... [2025-03-04 21:00:55.765581]
2025-03-04T21:00:59.0534595Z 
2025-03-04T21:00:59.0535772Z dynamo/test_input_attr_tracking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_input_attr_tracking_1.1_4c27fecbdc9abc30_.log
2025-03-04T21:00:59.0536573Z 
2025-03-04T21:00:59.0538099Z Running test_jiterator 1/1 ... [2025-03-04 21:00:59.053632]
2025-03-04T21:00:59.0538527Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:00:59.0541792Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:00:59.053953]
2025-03-04T21:01:02.4799667Z 
2025-03-04T21:01:02.4800641Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_c2ae29113bff11cf_.log
2025-03-04T21:01:02.4801515Z Running 0 items in this shard:
2025-03-04T21:01:02.4801719Z 
2025-03-04T21:01:02.4804672Z Running test_jit_fuser_te 1/1 ... [2025-03-04 21:01:02.480294]
2025-03-04T21:01:02.4805168Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:02.4808350Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_fuser_te.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'] ... [2025-03-04 21:01:02.480601]
2025-03-04T21:01:08.9033311Z 
2025-03-04T21:01:08.9034243Z test_jit_fuser_te 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_fuser_te_1.1_97d1b8c572d9a18c_.log
2025-03-04T21:01:08.9035004Z Running 0 items in this shard:
2025-03-04T21:01:08.9035205Z 
2025-03-04T21:01:08.9036577Z Running test_appending_byte_serializer 1/1 ... [2025-03-04 21:01:08.903490]
2025-03-04T21:01:08.9037084Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:08.9040296Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_appending_byte_serializer.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'] ... [2025-03-04 21:01:08.903802]
2025-03-04T21:01:12.1981610Z 
2025-03-04T21:01:12.1982723Z test_appending_byte_serializer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_appending_byte_serializer_1.1_00b6a36fb47508da_.log
2025-03-04T21:01:12.1983735Z 
2025-03-04T21:01:12.1984429Z Running functorch/test_ac 1/1 ... [2025-03-04 21:01:12.198250]
2025-03-04T21:01:12.1984901Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:12.1987792Z 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'] ... [2025-03-04 21:01:12.198555]
2025-03-04T21:01:15.9843393Z 
2025-03-04T21:01:15.9844459Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_c3558f7cbfb72469_.log
2025-03-04T21:01:15.9845137Z 
2025-03-04T21:01:15.9846408Z Running test_accelerator 1/1 ... [2025-03-04 21:01:15.984487]
2025-03-04T21:01:15.9846887Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:15.9850743Z 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'] ... [2025-03-04 21:01:15.984852]
2025-03-04T21:01:19.3635330Z 
2025-03-04T21:01:19.3636568Z test_accelerator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_accelerator_1.1_56b7a7bb4ba88bbb_.log
2025-03-04T21:01:19.3637594Z Running 0 items in this shard:
2025-03-04T21:01:19.3637799Z 
2025-03-04T21:01:19.3638335Z Running optim/test_optim 1/1 ... [2025-03-04 21:01:19.363684]
2025-03-04T21:01:19.3639003Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:19.3642379Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:19.363984]
2025-03-04T21:01:22.6270599Z 
2025-03-04T21:01:22.6271770Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_c5143e1aedb8a052_.log
2025-03-04T21:01:22.6272420Z 
2025-03-04T21:01:22.6351312Z Running dynamo/test_graph_break_messages 1/1 ... [2025-03-04 21:01:22.634827]
2025-03-04T21:01:22.6352088Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:22.6353702Z Running dynamo/test_export 1/1 ... [2025-03-04 21:01:22.635175]
2025-03-04T21:01:22.6354340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:22.6357161Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_break_messages.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'] ... [2025-03-04 21:01:22.635361]
2025-03-04T21:01:22.6359333Z Running dynamo/test_repros 1/1 ... [2025-03-04 21:01:22.635400]
2025-03-04T21:01:22.6360007Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:22.6361899Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:22.635651]
2025-03-04T21:01:22.6364224Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_repros.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:22.635901]
2025-03-04T21:01:26.2357303Z 
2025-03-04T21:01:26.2358655Z dynamo/test_graph_break_messages 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_break_messages_1.1_a54d6f8fcb5ef925_.log
2025-03-04T21:01:26.2360041Z 
2025-03-04T21:01:26.2434185Z 
2025-03-04T21:01:26.2435265Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_d87a064e32b04763_.log
2025-03-04T21:01:26.2435942Z 
2025-03-04T21:01:26.2452488Z 
2025-03-04T21:01:26.2453825Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_40223a5c24842b68_.log
2025-03-04T21:01:26.2454517Z 
2025-03-04T21:01:27.3843225Z Uploading artifacts took 1.15 seconds
2025-03-04T21:01:29.9940779Z Running dynamo/test_decorators 1/1 ... [2025-03-04 21:01:29.993599]
2025-03-04T21:01:29.9941684Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:29.9942363Z Running dynamo/test_optimizers 1/1 ... [2025-03-04 21:01:29.993671]
2025-03-04T21:01:29.9943110Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:29.9943656Z Running dynamo/test_minifier 1/1 ... [2025-03-04 21:01:29.993788]
2025-03-04T21:01:29.9944445Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:29.9945992Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_decorators.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'] ... [2025-03-04 21:01:29.993979]
2025-03-04T21:01:29.9948149Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:29.994052]
2025-03-04T21:01:29.9950421Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:29.994190]
2025-03-04T21:01:33.4893966Z 
2025-03-04T21:01:33.4895378Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_6707d74aa299c478_.log
2025-03-04T21:01:33.4896251Z 
2025-03-04T21:01:33.4896977Z 
2025-03-04T21:01:33.4897914Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_4b9c5f7fc1bc4da6_.log
2025-03-04T21:01:33.4898639Z 
2025-03-04T21:01:33.4903660Z 
2025-03-04T21:01:33.4905170Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_be30a4a939a6145b_.log
2025-03-04T21:01:33.4906524Z 
2025-03-04T21:01:37.0907520Z Running dynamo/test_backends 1/1 ... [2025-03-04 21:01:37.090306]
2025-03-04T21:01:37.0908728Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:37.0911083Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backends.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:37.090671]
2025-03-04T21:01:37.1132620Z Running dynamo/test_aot_autograd 1/1 ... [2025-03-04 21:01:37.112873]
2025-03-04T21:01:37.1133518Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:37.1135773Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:37.113242]
2025-03-04T21:01:37.1273399Z Running dynamo/test_functions 1/1 ... [2025-03-04 21:01:37.127011]
2025-03-04T21:01:37.1274357Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:37.1277267Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_functions.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'] ... [2025-03-04 21:01:37.127429]
2025-03-04T21:01:40.5802382Z 
2025-03-04T21:01:40.5803483Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_b4c05624317c19a1_.log
2025-03-04T21:01:40.5804201Z 
2025-03-04T21:01:41.1604670Z 
2025-03-04T21:01:41.1605900Z dynamo/test_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_functions_1.1_aeef6b18a1e558e7_.log
2025-03-04T21:01:41.1606612Z 
2025-03-04T21:01:41.1611711Z 
2025-03-04T21:01:41.1612743Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_6b275b85ac783778_.log
2025-03-04T21:01:41.1613444Z 
2025-03-04T21:01:44.2755763Z Running dynamo/test_skip_non_tensor 1/1 ... [2025-03-04 21:01:44.275175]
2025-03-04T21:01:44.2756367Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:44.2758512Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:44.275582]
2025-03-04T21:01:44.7727184Z Running dynamo/test_pre_dispatch 1/1 ... [2025-03-04 21:01:44.772269]
2025-03-04T21:01:44.7728096Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:44.7735991Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_pre_dispatch.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'] ... [2025-03-04 21:01:44.773160]
2025-03-04T21:01:44.8311550Z Running dynamo/test_python_autograd 1/1 ... [2025-03-04 21:01:44.830728]
2025-03-04T21:01:44.8312487Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:44.8318246Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:44.831390]
2025-03-04T21:01:47.8554202Z 
2025-03-04T21:01:47.8555613Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_8530e07b6b753139_.log
2025-03-04T21:01:47.8556801Z 
2025-03-04T21:01:48.3202525Z 
2025-03-04T21:01:48.3204468Z dynamo/test_python_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_python_autograd_1.1_7214439ed99fe7ac_.log
2025-03-04T21:01:48.3205878Z 
2025-03-04T21:01:48.3205905Z 
2025-03-04T21:01:48.3206969Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_e076652b44b4a8c8_.log
2025-03-04T21:01:48.3208052Z 
2025-03-04T21:01:51.5514694Z Running dynamo/test_exceptions 1/1 ... [2025-03-04 21:01:51.551020]
2025-03-04T21:01:51.5515323Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:51.5516781Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:51.551395]
2025-03-04T21:01:51.9648899Z Running dynamo/test_hooks 1/1 ... [2025-03-04 21:01:51.964407]
2025-03-04T21:01:51.9649688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:51.9653469Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_hooks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:51.964910]
2025-03-04T21:01:52.0388290Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2025-03-04 21:01:52.038386]
2025-03-04T21:01:52.0389398Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:52.0392970Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.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'] ... [2025-03-04 21:01:52.038846]
2025-03-04T21:01:55.0225283Z 
2025-03-04T21:01:55.0226403Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_382aa196860fe121_.log
2025-03-04T21:01:55.0227247Z 
2025-03-04T21:01:55.4786748Z 
2025-03-04T21:01:55.4787947Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_428f1b62efaa2c52_.log
2025-03-04T21:01:55.4788695Z 
2025-03-04T21:01:55.5404720Z 
2025-03-04T21:01:55.5406199Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_e3f563a91fd10554_.log
2025-03-04T21:01:55.5407618Z 
2025-03-04T21:01:58.7459675Z Running dynamo/test_base_output 1/1 ... [2025-03-04 21:01:58.745522]
2025-03-04T21:01:58.7460356Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:58.7462348Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:01:58.745953]
2025-03-04T21:01:59.1679839Z Running dynamo/test_reconstruct 1/1 ... [2025-03-04 21:01:59.167513]
2025-03-04T21:01:59.1680750Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:59.1683701Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_reconstruct.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'] ... [2025-03-04 21:01:59.167993]
2025-03-04T21:01:59.2033682Z Running dynamo/test_view 1/1 ... [2025-03-04 21:01:59.202902]
2025-03-04T21:01:59.2034683Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:01:59.2043068Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_view.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'] ... [2025-03-04 21:01:59.203886]
2025-03-04T21:02:02.2694408Z 
2025-03-04T21:02:02.2695584Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_8dd34aa970f58100_.log
2025-03-04T21:02:02.2696578Z 
2025-03-04T21:02:02.6643844Z 
2025-03-04T21:02:02.6645621Z dynamo/test_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reconstruct_1.1_14d69e6b02dbdc6b_.log
2025-03-04T21:02:02.6646915Z 
2025-03-04T21:02:02.6976914Z 
2025-03-04T21:02:02.6978234Z dynamo/test_view 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_view_1.1_41de2486f15d1150_.log
2025-03-04T21:02:02.6979169Z 
2025-03-04T21:02:05.9542088Z Running dynamo/test_trace_rules 1/1 ... [2025-03-04 21:02:05.953771]
2025-03-04T21:02:05.9542979Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:05.9545321Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:05.954184]
2025-03-04T21:02:06.2940131Z Running dynamo/test_compile 1/1 ... [2025-03-04 21:02:06.293566]
2025-03-04T21:02:06.2940937Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:06.2942884Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compile.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:06.293908]
2025-03-04T21:02:06.3138444Z Running dynamo/test_deviceguard 1/1 ... [2025-03-04 21:02:06.313382]
2025-03-04T21:02:06.3139173Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:06.3140459Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:06.313751]
2025-03-04T21:02:09.5052997Z 
2025-03-04T21:02:09.5054654Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_0d50fb37dbac9928_.log
2025-03-04T21:02:09.5055754Z 
2025-03-04T21:02:09.7457114Z 
2025-03-04T21:02:09.7458182Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_487eb031d8f17ec8_.log
2025-03-04T21:02:09.7458941Z 
2025-03-04T21:02:09.9119259Z 
2025-03-04T21:02:09.9120613Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_025f3b8d9e062024_.log
2025-03-04T21:02:13.2188886Z 
2025-03-04T21:02:13.2189814Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-03-04 21:02:13.218520]
2025-03-04T21:02:13.2190980Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:13.2193039Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backward_higher_order_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:13.218909]
2025-03-04T21:02:13.5248119Z Running dynamo/test_base_hop 1/1 ... [2025-03-04 21:02:13.524402]
2025-03-04T21:02:13.5248732Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:13.5250327Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_hop.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'] ... [2025-03-04 21:02:13.524768]
2025-03-04T21:02:13.5347790Z Running dynamo/test_bytecode_utils 1/1 ... [2025-03-04 21:02:13.534492]
2025-03-04T21:02:13.5348397Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:13.5351247Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:13.534830]
2025-03-04T21:02:16.6494992Z 
2025-03-04T21:02:16.6497018Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_920920bd9fd08515_.log
2025-03-04T21:02:16.6499005Z 
2025-03-04T21:02:17.0366601Z 
2025-03-04T21:02:17.0367959Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_ab3024ef557da339_.log
2025-03-04T21:02:17.0368716Z 
2025-03-04T21:02:17.5419314Z 
2025-03-04T21:02:17.5420239Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_6fe662202a6a4990_.log
2025-03-04T21:02:17.5420973Z 
2025-03-04T21:02:20.2901183Z Running dynamo/test_aot_autograd_cache 1/1 ... [2025-03-04 21:02:20.289646]
2025-03-04T21:02:20.2901888Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:20.2903650Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:20.290011]
2025-03-04T21:02:20.7316959Z Running dynamo/test_input_attr_tracking 1/1 ... [2025-03-04 21:02:20.731254]
2025-03-04T21:02:20.7318178Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:20.7320901Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_input_attr_tracking.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'] ... [2025-03-04 21:02:20.731769]
2025-03-04T21:02:21.1756844Z Running test_jiterator 1/1 ... [2025-03-04 21:02:21.175238]
2025-03-04T21:02:21.1757676Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:21.1761320Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:21.175750]
2025-03-04T21:02:24.2134756Z 
2025-03-04T21:02:24.2136211Z dynamo/test_input_attr_tracking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_input_attr_tracking_1.1_633cd8f535a59955_.log
2025-03-04T21:02:24.2137362Z 
2025-03-04T21:02:24.3451726Z 
2025-03-04T21:02:24.3453454Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_cac7f4597b0c6a5d_.log
2025-03-04T21:02:24.3455304Z 
2025-03-04T21:02:24.7736900Z 
2025-03-04T21:02:24.7738228Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_cae46d624fa56b33_.log
2025-03-04T21:02:24.7738999Z Running 0 items in this shard:
2025-03-04T21:02:24.7739205Z 
2025-03-04T21:02:27.9167099Z Running test_jit_fuser_te 1/1 ... [2025-03-04 21:02:27.916199]
2025-03-04T21:02:27.9167949Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:27.9169962Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_fuser_te.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'] ... [2025-03-04 21:02:27.916547]
2025-03-04T21:02:28.0255422Z Running test_appending_byte_serializer 1/1 ... [2025-03-04 21:02:28.025108]
2025-03-04T21:02:28.0256070Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:28.0257681Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_appending_byte_serializer.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'] ... [2025-03-04 21:02:28.025490]
2025-03-04T21:02:28.4620717Z Running functorch/test_ac 1/1 ... [2025-03-04 21:02:28.461617]
2025-03-04T21:02:28.4621603Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:28.4783919Z 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'] ... [2025-03-04 21:02:28.477851]
2025-03-04T21:02:31.5193123Z 
2025-03-04T21:02:31.5194564Z test_appending_byte_serializer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_appending_byte_serializer_1.1_084ce4b37309ff35_.log
2025-03-04T21:02:31.5195774Z 
2025-03-04T21:02:32.4671038Z 
2025-03-04T21:02:32.4672033Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_5065cb5e414fc7df_.log
2025-03-04T21:02:32.4672787Z 
2025-03-04T21:02:35.2009504Z Running test_accelerator 1/1 ... [2025-03-04 21:02:35.200468]
2025-03-04T21:02:35.2010366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:35.2011693Z 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'] ... [2025-03-04 21:02:35.200786]
2025-03-04T21:02:36.1769544Z Running optim/test_optim 1/1 ... [2025-03-04 21:02:36.176459]
2025-03-04T21:02:36.1770366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:02:36.1772301Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-03-04 21:02:36.176781]
2025-03-04T21:02:38.7565107Z 
2025-03-04T21:02:38.7566655Z test_accelerator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_accelerator_1.1_b952340f7095d5dd_.log
2025-03-04T21:02:38.7567452Z Running 0 items in this shard:
2025-03-04T21:02:38.7567654Z 
2025-03-04T21:02:39.6102070Z 
2025-03-04T21:02:39.6103164Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_856829bc0c37dcab_.log
2025-03-04T21:02:39.6103901Z 
2025-03-04T21:08:12.0918253Z 
2025-03-04T21:08:12.0919600Z test_jit_fuser_te 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_fuser_te_1.1_a1e7d06cb9db8837_.log
2025-03-04T21:08:12.4018456Z Running 6851 items in this shard: test/test_jit_fuser_te.py::TestFuserCommon::test_autodiff_fallback, test/test_jit_fuser_te.py::TestTEFuserStatic::test_abs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_adaptive_avg_pool2d, test/test_jit_fuser_te.py::TestTEFuserStatic::test_add_bool, test/test_jit_fuser_te.py::TestTEFuserStatic::test_addcmul, test/test_jit_fuser_te.py::TestTEFuserStatic::test_arg_configurations_smoke, test/test_jit_fuser_te.py::TestTEFuserStatic::test_autocast_down, test/test_jit_fuser_te.py::TestTEFuserStatic::test_autocast_up, test/test_jit_fuser_te.py::TestTEFuserStatic::test_batch_norm, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_div_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_pow, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_scalar_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_tensor_scalar_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_bitwise_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_broadcast, test/test_jit_fuser_te.py::TestTEFuserStatic::test_cat_2k_args, test/test_jit_fuser_te.py::TestTEFuserStatic::test_cat_graph_opt, test/test_jit_fuser_te.py::TestTEFuserStatic::test_channels_last_dims_dynamic, test/test_jit_fuser_te.py::TestTEFuserStatic::test_checks_cat_inputs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk_correctness, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk_distributes, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk_motion_deduplicates_inputs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk_mul_one, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk_multiple, test/test_jit_fuser_te.py::TestTEFuserStatic::test_clamp, test/test_jit_fuser_te.py::TestTEFuserStatic::test_clamp_double, test/test_jit_fuser_te.py::TestTEFuserStatic::test_clamp_int, test/test_jit_fuser_te.py::TestTEFuserStatic::test_comparison_eq_ne, test/test_jit_fuser_te.py::TestTEFuserStatic::test_comparison_ge_le, test/test_jit_fuser_te.py::TestTEFuserStatic::test_comparison_gt_lt, test/test_jit_fuser_te.py::TestTEFuserStatic::test_concat, test/test_jit_fuser_te.py::TestTEFuserStatic::test_concat_invariant, test/test_jit_fuser_te.py::TestTEFuserStatic::test_constant_chunk_shapes, test/test_jit_fuser_te.py::TestTEFuserStatic::test_conv2d, test/test_jit_fuser_te.py::TestTEFuserStatic::test_conv2d_depthwise, test/test_jit_fuser_te.py::TestTEFuserStatic::test_cuda_half, test/test_jit_fuser_te.py::TestTEFuserStatic::test_dims, test/test_jit_fuser_te.py::TestTEFuserStatic::test_disabled, test/test_jit_fuser_te.py::TestTEFuserStatic::test_div_bool, test/test_jit_fuser_te.py::TestTEFuserStatic::test_dynamic_cat, test/test_jit_fuser_te.py::TestTEFuserStatic::test_dynamic_shapes, test/test_jit_fuser_te.py::TestTEFuserStatic::test_eq_unsqueeze_type_as, test/test_jit_fuser_te.py::TestTEFuserStatic::test_erf, test/test_jit_fuser_te.py::TestTEFuserStatic::test_exhaust_specializations, test/test_jit_fuser_te.py::TestTEFuserStatic::test_exp, test/test_jit_fuser_te.py::TestTEFuserStatic::test_fusion_reuse_multi_gpu, test/test_jit_fuser_te.py::TestTEFuserStatic::test_gelu, test/test_jit_fuser_te.py::TestTEFuserStatic::test_hardsigmoid_fwd_bwd, test/test_jit_fuser_te.py::TestTEFuserStatic::test_hardswish_fwd_bwd, test/test_jit_fuser_te.py::TestTEFuserStatic::test_inlined_optimized_graph, test/test_jit_fuser_te.py::TestTEFuserStatic::test_isnan, test/test_jit_fuser_te.py::TestTEFuserStatic::test_kernel_cache_multi_gpu, test/test_jit_fuser_te.py::TestTEFuserStatic::test_lerp, test/test_jit_fuser_te.py::TestTEFuserStatic::test_list_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_lstm, test/test_jit_fuser_te.py::TestTEFuserStatic::test_lstm_concat, test/test_jit_fuser_te.py::TestTEFuserStatic::test_lstm_gates_permutations, test/test_jit_fuser_te.py::TestTEFuserStatic::test_lstm_traced, test/test_jit_fuser_te.py::TestTEFuserStatic::test_masked_fill, test/test_jit_fuser_te.py::TestTEFuserStatic::test_matmul, test/test_jit_fuser_te.py::TestTEFuserStatic::test_milstm, test/test_jit_fuser_te.py::TestTEFuserStatic::test_minmax, test/test_jit_fuser_te.py::TestTEFuserStatic::test_minmax_int_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_mul_bool, test/test_jit_fuser_te.py::TestTEFuserStatic::test_neg_pow, test/test_jit_fuser_te.py::TestTEFuserStatic::test_nonzero_device_cuda, test/test_jit_fuser_te.py::TestTEFuserStatic::test_nop, test/test_jit_fuser_te.py::TestTEFuserStatic::test_pow_multiple_dtype, test/test_jit_fuser_te.py::TestTEFuserStatic::test_profiler, test/test_jit_fuser_te.py::TestTEFuserStatic::test_rand_broadcast_cuda, test/test_jit_fuser_te.py::TestTEFuserStatic::test_rand_cuda, test/test_jit_fuser_te.py::TestTEFuserStatic::test_rand_diamond, test/test_jit_fuser_te.py::TestTEFuserStatic::test_relu, test/test_jit_fuser_te.py::TestTEFuserStatic::test_relu_fwd_bwd, test/test_jit_fuser_te.py::TestTEFuserStatic::test_remove_output_used_only_in_size, test/test_jit_fuser_te.py::TestTEFuserStatic::test_scalar, test/test_jit_fuser_te.py::TestTEFuserStatic::test_scalar_arg, test/test_jit_fuser_te.py::TestTEFuserStatic::test_scalar_only_inputs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_skip_grad_in_check, test/test_jit_fuser_te.py::TestTEFuserStatic::test_small_constant, test/test_jit_fuser_te.py::TestTEFuserStatic::test_sub_gt_and, test/test_jit_fuser_te.py::TestTEFuserStatic::test_sum_dim, test/test_jit_fuser_te.py::TestTEFuserStatic::test_sum_keepdim_cast, test/test_jit_fuser_te.py::TestTEFuserStatic::test_sum_simple, test/test_jit_fuser_te.py::TestTEFuserStatic::test_superslomo, test/test_jit_fuser_te.py::TestTEFuserStatic::test_tensor_scalar_ops, 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test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_special_zeta_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_split_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_split_list_args_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_split_with_sizes_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_split_with_sizes_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_square_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_squeeze_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_squeeze_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_squeeze_multiple_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_stack_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_std_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_std_mean_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_std_mean_unbiased_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_std_unbiased_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_stft_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_sum_to_size_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_svd_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_svd_lowrank_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_t_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_take_along_dim_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_take_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_tensor_split_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_tensordot_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_tile_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_to_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_to_sparse_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_topk_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_trace_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_transpose_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_trapezoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_trapz_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_triangular_solve_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_tril_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_triu_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unbind_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unbind_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unflatten_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unfold_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unfold_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_uniform_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unique_consecutive_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unique_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unsafe_chunk_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unsafe_split_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_unsqueeze_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_var_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_var_mean_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_var_mean_unbiased_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_var_unbiased_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_vdot_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_view_as_complex_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_view_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_vsplit_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_vstack_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_xlogy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_zero__cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_zeros_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_zeros_like_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working___radd___cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working___rdiv___cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working___rmod___cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working___rmul___cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_abs_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_acos_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_add_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_addcmul_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_addmm_decomposed_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_asin_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_atan2_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_atan_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_bool_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_byte_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_ceil_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_char_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_clamp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_contiguous_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_cos_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_cosh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_floor_rounding_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_no_rounding_mode_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_trunc_rounding_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_double_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_eq_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_erf_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_erfc_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_exp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expand_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expand_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expm1_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_float_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_floor_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_fmod_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_ge_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_gt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_half_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_int_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_isnan_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_le_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lerp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lgamma_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log10_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log1p_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log2_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_long_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_masked_fill_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_max_binary_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mean_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_min_binary_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mm_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mul_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_ne_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_neg_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardshrink_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardsigmoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardswish_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardtanh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_leaky_relu_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_relu6_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_relu_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_softplus_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_softsign_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_tanhshrink_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_threshold_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_permute_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_pow_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_reciprocal_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_remainder_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_reshape_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_reshape_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_round_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_rsqrt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_rsub_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_short_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sigmoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sign_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sin_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sinh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sqrt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sub_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sum_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_t_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_tan_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_tanh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_transpose_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_true_divide_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_trunc_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_unsqueeze_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_view_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_view_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_where_cpu_float32, test/test_jit_fuser_te.py::TestLoopnestRandomizationCPU::test_relu_cpu
2025-03-04T21:08:12.6501003Z 
2025-03-04T21:08:12.9236858Z Running test batch 'tests to run' cost 3637.4 seconds
2025-03-04T21:08:13.8800075Z 
2025-03-04T21:08:13.8800779Z real	60m43.242s
2025-03-04T21:08:13.8801103Z user	81m48.950s
2025-03-04T21:08:13.8801361Z sys	8m0.094s
2025-03-04T21:08:13.8801624Z + assert_git_not_dirty
2025-03-04T21:08:13.8805602Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-04T21:08:13.8810842Z + [[ linux-focal-py3.13-clang10 != *xla* ]]
2025-03-04T21:08:13.8834225Z ++ git status --porcelain
2025-03-04T21:08:13.8834871Z ++ grep -v '?? third_party'
2025-03-04T21:08:50.7241758Z ++ true
2025-03-04T21:08:50.7297709Z + git_status=
2025-03-04T21:08:50.7298122Z + [[ -n '' ]]
2025-03-04T21:08:50.7298380Z + [[ 1 == 1 ]]
2025-03-04T21:08:50.7298647Z + test_aten
2025-03-04T21:08:50.7303826Z + echo 'Running ATen tests with pytorch lib'
2025-03-04T21:08:50.7304327Z Running ATen tests with pytorch lib
2025-03-04T21:08:50.7304653Z + [[ -n '' ]]
2025-03-04T21:08:50.7304956Z + echo 'Running test with the build folder'
2025-03-04T21:08:50.7305388Z Running test with the build folder
2025-03-04T21:08:50.7305748Z + TEST_BASE_DIR=build/bin
2025-03-04T21:08:50.7306451Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin
2025-03-04T21:08:50.7345116Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin
2025-03-04T21:08:50.7354522Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin
2025-03-04T21:08:50.7363414Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin
2025-03-04T21:08:50.7380169Z + 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
2025-03-04T21:08:50.7387513Z + ls build/bin
2025-03-04T21:08:50.7454938Z  BackoffTest			       cpu_generator_test
2025-03-04T21:08:50.7455760Z  CMakeFiles			       cpu_profiling_allocator_test
2025-03-04T21:08:50.7456371Z  CTestTestfile.cmake		       cpu_rng_test
2025-03-04T21:08:50.7456870Z  CppSignature_test		       dispatch_key_set_test
2025-03-04T21:08:50.7457842Z  Dict_test			       dlconvertor_test
2025-03-04T21:08:50.7458204Z  Dimname_test			       example_allreduce
2025-03-04T21:08:50.7458666Z  FileStoreTest			       extension_backend_test
2025-03-04T21:08:50.7459069Z  HashStoreTest			       half_test
2025-03-04T21:08:50.7459465Z  IListRef_test			       inline_container_test
2025-03-04T21:08:50.7459856Z  KernelFunction_test		       ivalue_test
2025-03-04T21:08:50.7460237Z  List_test			       kernel_function_legacy_test
2025-03-04T21:08:50.7460621Z  Makefile			       kernel_function_test
2025-03-04T21:08:50.7461009Z  MaybeOwned_test		       kernel_lambda_legacy_test
2025-03-04T21:08:50.7461428Z  NamedTensor_test		       kernel_lambda_test
2025-03-04T21:08:50.7461852Z  ProcessGroupGlooTest		       kernel_stackbased_test
2025-03-04T21:08:50.7462274Z  StorageUtils_test		       lazy_tensor_test
2025-03-04T21:08:50.7462642Z  TCPStoreTest			       legacy_vmap_test
2025-03-04T21:08:50.7463000Z  aot_model_compiler_test	       libc10.so
2025-03-04T21:08:50.7463363Z  apply_utils_test		      'libcaffe2*'
2025-03-04T21:08:50.7463686Z  atest				      'libmkldnn*'
2025-03-04T21:08:50.7464007Z  backend_fallback_test		      'libnccl*'
2025-03-04T21:08:50.7464348Z  basic				       libtorch.so
2025-03-04T21:08:50.7464666Z  broadcast_test			       libtorch_cpu.so
2025-03-04T21:08:50.7465184Z  c10_ArrayRef_test		       libtorch_global_deps.so
2025-03-04T21:08:50.7465587Z  c10_Bitset_test		       libtorch_python.so
2025-03-04T21:08:50.7466030Z  c10_CompileTimeFunctionPointer_test   libtorchbind_test.so
2025-03-04T21:08:50.7466569Z  c10_ConstexprCrc_test		       make_boxed_from_unboxed_functor_test
2025-03-04T21:08:50.7467164Z  c10_DeadlockDetection_test	       math_kernel_test
2025-03-04T21:08:50.7467584Z  c10_DeviceGuard_test		       memory_format_test
2025-03-04T21:08:50.7467993Z  c10_Device_test		       memory_overlapping_test
2025-03-04T21:08:50.7468420Z  c10_DispatchKeySet_test	       mobile_memory_cleanup
2025-03-04T21:08:50.7468817Z  c10_Half_test			       native_test
2025-03-04T21:08:50.7469203Z  c10_InlineDeviceGuard_test	       op_allowlist_test
2025-03-04T21:08:50.7469658Z  c10_InlineStreamGuard_test	       op_registration_test
2025-03-04T21:08:50.7470091Z  c10_LeftRight_test		       operator_name_test
2025-03-04T21:08:50.7470493Z  c10_Metaprogramming_test	       operators_test
2025-03-04T21:08:50.7470932Z  c10_NetworkFlow_test		       packedtensoraccessor_test
2025-03-04T21:08:50.7471356Z  c10_Scalar_test		       parallel_benchmark
2025-03-04T21:08:50.7471729Z  c10_SizesAndStrides_test	       pow_test
2025-03-04T21:08:50.7472086Z  c10_StreamGuard_test		       protoc
2025-03-04T21:08:50.7472422Z  c10_SymInt_test		       protoc-3.13.0.0
2025-03-04T21:08:50.7472792Z  c10_Synchronized_test		       quantized_test
2025-03-04T21:08:50.7473234Z  c10_ThreadLocal_test		       reduce_ops_test
2025-03-04T21:08:50.7473852Z  c10_TypeIndex_test		       reportMemoryUsage_test
2025-03-04T21:08:50.7474268Z  c10_TypeList_test		       scalar_tensor_test
2025-03-04T21:08:50.7474650Z  c10_TypeTraits_test		       scalar_test
2025-03-04T21:08:50.7475030Z  c10_accumulate_test		       static_runtime_bench
2025-03-04T21:08:50.7475432Z  c10_bfloat16_test		       static_runtime_test
2025-03-04T21:08:50.7475829Z  c10_bit_cast_test		       stride_properties_test
2025-03-04T21:08:50.7476242Z  c10_complex_math_test		       tensor_iterator_test
2025-03-04T21:08:50.7476628Z  c10_complex_test		       test_api
2025-03-04T21:08:50.7476959Z  c10_cow_test			       test_cpp_rpc
2025-03-04T21:08:50.7477302Z  c10_error_test			       test_dist_autograd
2025-03-04T21:08:50.7477708Z  c10_exception_test		       test_edge_op_registration
2025-03-04T21:08:50.7478098Z  c10_flags_test			       test_jit
2025-03-04T21:08:50.7478429Z  c10_generic_math_test		       test_lazy
2025-03-04T21:08:50.7478816Z  c10_intrusive_ptr_benchmark	       test_mobile_nnc
2025-03-04T21:08:50.7479224Z  c10_intrusive_ptr_test		       test_parallel
2025-03-04T21:08:50.7479640Z  c10_irange_test		       test_tensorexpr
2025-03-04T21:08:50.7480046Z  c10_lazy_test			       thread_init_test
2025-03-04T21:08:50.7480407Z  c10_logging_test		       torch_shm_manager
2025-03-04T21:08:50.7480789Z  c10_optional_test		       tutorial_tensorexpr
2025-03-04T21:08:50.7481206Z  c10_ordered_preserving_dict_test      type_ptr_test
2025-03-04T21:08:50.7481596Z  c10_registry_test		       type_test
2025-03-04T21:08:50.7481971Z  c10_small_vector_test		       undefined_tensor_test
2025-03-04T21:08:50.7482381Z  c10_ssize_test			       vec_test_all_types_AVX2
2025-03-04T21:08:50.7482796Z  c10_string_util_test		       vec_test_all_types_AVX512
2025-03-04T21:08:50.7483239Z  c10_string_view_test		       vec_test_all_types_DEFAULT
2025-03-04T21:08:50.7483675Z  c10_tempfile_test		       verify_api_visibility
2025-03-04T21:08:50.7484054Z  c10_typeid_test		       weakref_test
2025-03-04T21:08:50.7484407Z  cmake_install.cmake		       wrapdim_test
2025-03-04T21:08:50.7484777Z  cpu_allocator_test		       xla_tensor_test
2025-03-04T21:08:50.7485141Z + aten/tools/run_tests.sh build/bin
2025-03-04T21:08:50.7489155Z + set -e
2025-03-04T21:08:50.7491850Z ++ dirname aten/tools/run_tests.sh
2025-03-04T21:08:50.7506956Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup
2025-03-04T21:08:50.7507543Z + export CPP_TESTS_DIR=build/bin
2025-03-04T21:08:50.7507875Z + CPP_TESTS_DIR=build/bin
2025-03-04T21:08:50.7508206Z + VALGRIND=ON
2025-03-04T21:08:50.7510196Z + 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
2025-03-04T21:08:50.8538878Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2025-03-04T21:08:50.8539832Z   import pkg_resources
2025-03-04T21:08:54.6015916Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2025-03-04T21:08:54.6219855Z Found test times from artifacts
2025-03-04T21:08:54.6998742Z Found test times from artifacts
2025-03-04T21:08:54.7020275Z Running all tests
2025-03-04T21:08:54.7024723Z Running parallel tests on 3 processes
2025-03-04T21:08:54.7026384Z Name: tests to run (est. time: 0.0min)
2025-03-04T21:08:54.7026812Z   Serial tests (0):
2025-03-04T21:08:54.7027086Z   Parallel tests (19):
2025-03-04T21:08:54.7027367Z     cpp/Dict_test 1/1
2025-03-04T21:08:54.7027666Z     cpp/Dimname_test 1/1
2025-03-04T21:08:54.7027967Z     cpp/NamedTensor_test 1/1
2025-03-04T21:08:54.7028464Z     cpp/apply_utils_test 1/1
2025-03-04T21:08:54.7028755Z     cpp/atest 1/1
2025-03-04T21:08:54.7029016Z     cpp/basic 1/1
2025-03-04T21:08:54.7029299Z     cpp/broadcast_test 1/1
2025-03-04T21:08:54.7029618Z     cpp/cpu_generator_test 1/1
2025-03-04T21:08:54.7029939Z     cpp/dlconvertor_test 1/1
2025-03-04T21:08:54.7030259Z     cpp/extension_backend_test 1/1
2025-03-04T21:08:54.7030590Z     cpp/lazy_tensor_test 1/1
2025-03-04T21:08:54.7030910Z     cpp/legacy_vmap_test 1/1
2025-03-04T21:08:54.7031209Z     cpp/native_test 1/1
2025-03-04T21:08:54.7031495Z     cpp/operators_test 1/1
2025-03-04T21:08:54.7031809Z     cpp/scalar_tensor_test 1/1
2025-03-04T21:08:54.7032115Z     cpp/scalar_test 1/1
2025-03-04T21:08:54.7032401Z     cpp/tensor_iterator_test 1/1
2025-03-04T21:08:54.7032734Z     cpp/undefined_tensor_test 1/1
2025-03-04T21:08:54.7033061Z     cpp/wrapdim_test 1/1
2025-03-04T21:08:54.7033368Z Name: excluded (est. time: 0.0min)
2025-03-04T21:08:54.7033691Z   Serial tests (0):
2025-03-04T21:08:54.7033960Z   Parallel tests (0):
2025-03-04T21:08:54.7087697Z Running cpp/Dict_test 1/1 ... [2025-03-04 21:08:54.708448]
2025-03-04T21:08:54.7088446Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:08:54.7102260Z 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-7607452af12c0391.xml', '-x', '--reruns=2'] ... [2025-03-04 21:08:54.709115]
2025-03-04T21:08:57.5306178Z 
2025-03-04T21:08:57.5307312Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_9fc6005578b9e0d7_.log
2025-03-04T21:08:57.5308199Z 
2025-03-04T21:08:57.5309124Z Running cpp/Dimname_test 1/1 ... [2025-03-04 21:08:57.530633]
2025-03-04T21:08:57.5309928Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:08:57.5314536Z 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-d212854796f313c3.xml', '-x', '--reruns=2'] ... [2025-03-04 21:08:57.531110]
2025-03-04T21:08:59.1487502Z 
2025-03-04T21:08:59.1488546Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_6e8a298ffb1be184_.log
2025-03-04T21:08:59.1489244Z 
2025-03-04T21:08:59.1489473Z Running cpp/NamedTensor_test 1/1 ... [2025-03-04 21:08:59.148643]
2025-03-04T21:08:59.1489936Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:08:59.1492586Z 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-3b38a93f9bc07a26.xml', '-x', '--reruns=2'] ... [2025-03-04 21:08:59.148995]
2025-03-04T21:09:00.7659667Z 
2025-03-04T21:09:00.7661074Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_8af96018782e7643_.log
2025-03-04T21:09:00.7661780Z 
2025-03-04T21:09:00.7662018Z Running cpp/apply_utils_test 1/1 ... [2025-03-04 21:09:00.765826]
2025-03-04T21:09:00.7662521Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:00.7664431Z 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-1b43d71462888da1.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:00.766199]
2025-03-04T21:09:02.4335009Z 
2025-03-04T21:09:02.4336411Z 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_b65ae24d9acf615c_.log
2025-03-04T21:09:02.4337483Z 
2025-03-04T21:09:02.4337838Z Running cpp/atest 1/1 ... [2025-03-04 21:09:02.433359]
2025-03-04T21:09:02.4338483Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:02.4340915Z 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-9dd267e7195e4e5e.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:02.433751]
2025-03-04T21:09:04.1011559Z 
2025-03-04T21:09:04.1012924Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_1db38d63b5cf795b_.log
2025-03-04T21:09:04.1013532Z 
2025-03-04T21:09:04.1013767Z Running cpp/basic 1/1 ... [2025-03-04 21:09:04.100959]
2025-03-04T21:09:04.1014175Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:04.1015875Z 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-36bd3185c417c048.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:04.101325]
2025-03-04T21:09:05.7683184Z 
2025-03-04T21:09:05.7684595Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_bd7c7cf7e9157da1_.log
2025-03-04T21:09:05.7685538Z 
2025-03-04T21:09:05.7685877Z Running cpp/broadcast_test 1/1 ... [2025-03-04 21:09:05.768155]
2025-03-04T21:09:05.7686540Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:05.7688967Z 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-8984e353ecae438b.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:05.768540]
2025-03-04T21:09:07.3857181Z 
2025-03-04T21:09:07.3858618Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_d19d47621cf8a219_.log
2025-03-04T21:09:07.3859294Z 
2025-03-04T21:09:07.3859535Z Running cpp/cpu_generator_test 1/1 ... [2025-03-04 21:09:07.385598]
2025-03-04T21:09:07.3860044Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:07.3862130Z 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-1c2499c7e2fb650e.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:07.385951]
2025-03-04T21:09:09.0030569Z 
2025-03-04T21:09:09.0031610Z 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_c988927da08edac1_.log
2025-03-04T21:09:09.0032324Z 
2025-03-04T21:09:09.0032542Z Running cpp/dlconvertor_test 1/1 ... [2025-03-04 21:09:09.002920]
2025-03-04T21:09:09.0034208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:09.0035459Z 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-333a13429908a6c7.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:09.003253]
2025-03-04T21:09:10.6204305Z 
2025-03-04T21:09:10.6205712Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_e4b01fb443966257_.log
2025-03-04T21:09:10.6206830Z 
2025-03-04T21:09:10.6207218Z Running cpp/extension_backend_test 1/1 ... [2025-03-04 21:09:10.620258]
2025-03-04T21:09:10.6208020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:10.6210157Z 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-37a18bd3bb3a37e1.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:10.620636]
2025-03-04T21:09:12.2378950Z 
2025-03-04T21:09:12.2380659Z 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_c134e007ba8d9a20_.log
2025-03-04T21:09:12.2381890Z 
2025-03-04T21:09:12.2382216Z Running cpp/lazy_tensor_test 1/1 ... [2025-03-04 21:09:12.237741]
2025-03-04T21:09:12.2383251Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:12.2385204Z 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-75685ade51c384f5.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:12.238131]
2025-03-04T21:09:13.8553822Z 
2025-03-04T21:09:13.8554776Z 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_1660bc018d21a8a4_.log
2025-03-04T21:09:13.8555465Z 
2025-03-04T21:09:13.8555706Z Running cpp/legacy_vmap_test 1/1 ... [2025-03-04 21:09:13.855232]
2025-03-04T21:09:13.8556200Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:13.8557679Z 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-1bfe850fd0fd24a1.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:13.855560]
2025-03-04T21:09:15.4727415Z 
2025-03-04T21:09:15.4728389Z 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_db82642df822766d_.log
2025-03-04T21:09:15.4729701Z 
2025-03-04T21:09:15.4729924Z Running cpp/native_test 1/1 ... [2025-03-04 21:09:15.472608]
2025-03-04T21:09:15.4730520Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:15.4731796Z 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-6f92e162ef9253ff.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:15.472949]
2025-03-04T21:09:17.0900447Z 
2025-03-04T21:09:17.0901967Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_cf1f93dcafb1ec50_.log
2025-03-04T21:09:17.0903014Z 
2025-03-04T21:09:17.0903350Z Running cpp/operators_test 1/1 ... [2025-03-04 21:09:17.089903]
2025-03-04T21:09:17.0904020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:17.0905796Z 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-c3194177e01aecd3.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:17.090298]
2025-03-04T21:09:18.7076500Z 
2025-03-04T21:09:18.7077881Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_94c46a3c565350d9_.log
2025-03-04T21:09:18.7078822Z 
2025-03-04T21:09:18.7079059Z Running cpp/scalar_tensor_test 1/1 ... [2025-03-04 21:09:18.707488]
2025-03-04T21:09:18.7079531Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:18.7081291Z 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-27de0bbbc5843243.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:18.707845]
2025-03-04T21:09:20.3252849Z 
2025-03-04T21:09:20.3254138Z 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_59ec1100fee095ee_.log
2025-03-04T21:09:20.3254873Z 
2025-03-04T21:09:20.3255092Z Running cpp/scalar_test 1/1 ... [2025-03-04 21:09:20.325122]
2025-03-04T21:09:20.3255583Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:20.3257309Z 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-fa4813567ed62aaf.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:20.325481]
2025-03-04T21:09:21.9425242Z 
2025-03-04T21:09:21.9426272Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_a353d035b4db374a_.log
2025-03-04T21:09:21.9426953Z 
2025-03-04T21:09:21.9427477Z Running cpp/tensor_iterator_test 1/1 ... [2025-03-04 21:09:21.942420]
2025-03-04T21:09:21.9427956Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:21.9430095Z 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-a071992cc7e449c9.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:21.942773]
2025-03-04T21:09:23.5597844Z 
2025-03-04T21:09:23.5599284Z 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_2f6801378270ebc5_.log
2025-03-04T21:09:23.5600458Z 
2025-03-04T21:09:23.5600851Z Running cpp/undefined_tensor_test 1/1 ... [2025-03-04 21:09:23.559689]
2025-03-04T21:09:23.5601597Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:23.5604108Z 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-78803bcf306b0db0.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:23.560090]
2025-03-04T21:09:25.1771830Z 
2025-03-04T21:09:25.1773496Z 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_ccdb19b2744d023b_.log
2025-03-04T21:09:25.1774960Z 
2025-03-04T21:09:25.1775273Z Running cpp/wrapdim_test 1/1 ... [2025-03-04 21:09:25.176961]
2025-03-04T21:09:25.1776050Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:25.1778179Z 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-54f9ddd3526b816a.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:25.177341]
2025-03-04T21:09:26.7948940Z 
2025-03-04T21:09:26.7949931Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_704c6acaceaa295c_.log
2025-03-04T21:09:26.7950668Z 
2025-03-04T21:09:26.7959269Z Running cpp/Dict_test 1/1 ... [2025-03-04 21:09:26.795694]
2025-03-04T21:09:26.7959777Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:26.7961090Z Running cpp/Dimname_test 1/1 ... [2025-03-04 21:09:26.795923]
2025-03-04T21:09:26.7961865Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:26.7962484Z Running cpp/NamedTensor_test 1/1 ... [2025-03-04 21:09:26.795974]
2025-03-04T21:09:26.7962964Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:26.7965865Z 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-3eee5484414e0482.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:26.796284]
2025-03-04T21:09:26.7968458Z 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-e67f2393538f0c78.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:26.796524]
2025-03-04T21:09:26.7970561Z 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-17d17ca2c3974e91.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:26.796577]
2025-03-04T21:09:30.6690626Z 
2025-03-04T21:09:30.6691897Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_2d085128394105a9_.log
2025-03-04T21:09:30.6692970Z 
2025-03-04T21:09:31.6460223Z 
2025-03-04T21:09:31.6462849Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_f4fe54826e1a19aa_.log
2025-03-04T21:09:31.6464249Z 
2025-03-04T21:09:34.8892198Z Running cpp/apply_utils_test 1/1 ... [2025-03-04 21:09:34.888723]
2025-03-04T21:09:34.8893417Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:34.8901943Z 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-e1fe8bc1d16ce9d0.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:34.889256]
2025-03-04T21:09:35.3856334Z Running cpp/atest 1/1 ... [2025-03-04 21:09:35.385147]
2025-03-04T21:09:35.3857135Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:35.3861045Z 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-2a8f349f79f3bcb3.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:35.385630]
2025-03-04T21:09:37.9812593Z 
2025-03-04T21:09:37.9813976Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_451119c32606459c_.log
2025-03-04T21:09:37.9815172Z 
2025-03-04T21:09:38.7700235Z 
2025-03-04T21:09:38.7701820Z 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_2720891fa2fe657f_.log
2025-03-04T21:09:38.7703194Z 
2025-03-04T21:09:40.7617196Z 
2025-03-04T21:09:40.7618507Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_88948ccedfdaad69_.log
2025-03-04T21:09:40.7619125Z 
2025-03-04T21:09:42.0391533Z Running cpp/basic 1/1 ... [2025-03-04 21:09:42.038799]
2025-03-04T21:09:42.0392341Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:42.0399143Z 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-97cb607fe29021bd.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:42.039549]
2025-03-04T21:09:42.6287563Z Running cpp/broadcast_test 1/1 ... [2025-03-04 21:09:42.628273]
2025-03-04T21:09:42.6288439Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:42.6290991Z 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-6adeb1b30f588ec4.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:42.628723]
2025-03-04T21:09:44.5681622Z Running cpp/cpu_generator_test 1/1 ... [2025-03-04 21:09:44.567729]
2025-03-04T21:09:44.5682369Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:44.5686645Z 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-cd9b59b20f551e9a.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:44.568245]
2025-03-04T21:09:45.4556373Z 
2025-03-04T21:09:45.4557968Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_ebd40b0709a2f18e_.log
2025-03-04T21:09:45.4559248Z 
2025-03-04T21:09:45.5814426Z 
2025-03-04T21:09:45.5815626Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_b0704dcb83617e92_.log
2025-03-04T21:09:45.5816611Z 
2025-03-04T21:09:49.4025712Z 
2025-03-04T21:09:49.4027158Z 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_bd629cb135206cb7_.log
2025-03-04T21:09:49.4028360Z 
2025-03-04T21:09:49.4575083Z Running cpp/dlconvertor_test 1/1 ... [2025-03-04 21:09:49.457095]
2025-03-04T21:09:49.4575954Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:49.4579646Z 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-6f849a33d9deb5a3.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:49.457576]
2025-03-04T21:09:49.4696448Z Running cpp/extension_backend_test 1/1 ... [2025-03-04 21:09:49.469280]
2025-03-04T21:09:49.4697281Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:49.4701267Z 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-b7668fde21ff4ab2.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:49.469746]
2025-03-04T21:09:52.1271115Z 
2025-03-04T21:09:52.1272135Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_a37a6605d03e4926_.log
2025-03-04T21:09:52.1272837Z 
2025-03-04T21:09:52.1391389Z 
2025-03-04T21:09:52.1392555Z 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_d83c2e06af42610d_.log
2025-03-04T21:09:52.1393327Z 
2025-03-04T21:09:53.6420843Z Running cpp/lazy_tensor_test 1/1 ... [2025-03-04 21:09:53.641643]
2025-03-04T21:09:53.6421728Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:53.6425195Z 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-480ac9f6b6381436.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:53.642146]
2025-03-04T21:09:55.7939476Z Running cpp/legacy_vmap_test 1/1 ... [2025-03-04 21:09:55.793538]
2025-03-04T21:09:55.7940162Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:55.7943922Z 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-68421525c917a134.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:55.794039]
2025-03-04T21:09:56.0108244Z 
2025-03-04T21:09:56.0109302Z 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_0fecc2aef53f444e_.log
2025-03-04T21:09:56.0110056Z 
2025-03-04T21:09:56.0426058Z Running cpp/native_test 1/1 ... [2025-03-04 21:09:56.042285]
2025-03-04T21:09:56.0426553Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:09:56.0429756Z 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-92a275b927f7b0b4.xml', '-x', '--reruns=2'] ... [2025-03-04 21:09:56.042714]
2025-03-04T21:09:58.9635260Z 
2025-03-04T21:09:58.9637119Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_047c4d6c0e3e5f26_.log
2025-03-04T21:09:58.9638387Z 
2025-03-04T21:10:00.2356615Z Running cpp/operators_test 1/1 ... [2025-03-04 21:10:00.235233]
2025-03-04T21:10:00.2357789Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:10:00.2361841Z 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-2f06fa238042999b.xml', '-x', '--reruns=2'] ... [2025-03-04 21:10:00.235769]
2025-03-04T21:10:02.0199726Z 
2025-03-04T21:10:02.0201195Z 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_42cbb8adfd2ff7f9_.log
2025-03-04T21:10:02.0202389Z 
2025-03-04T21:10:03.1067454Z 
2025-03-04T21:10:03.1068960Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_deae15d35d576758_.log
2025-03-04T21:10:03.1069992Z 
2025-03-04T21:10:03.3288942Z Running cpp/scalar_tensor_test 1/1 ... [2025-03-04 21:10:03.328476]
2025-03-04T21:10:03.3289672Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:10:03.3293306Z 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-57dc20e4ebef243d.xml', '-x', '--reruns=2'] ... [2025-03-04 21:10:03.328980]
2025-03-04T21:10:05.7478811Z 
2025-03-04T21:10:05.7480235Z 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_56bfd3ec51b9bbee_.log
2025-03-04T21:10:05.7481097Z 
2025-03-04T21:10:05.9849111Z Running cpp/scalar_test 1/1 ... [2025-03-04 21:10:05.984492]
2025-03-04T21:10:05.9849826Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:10:05.9853298Z 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-3ccf95741bfb8965.xml', '-x', '--reruns=2'] ... [2025-03-04 21:10:05.984961]
2025-03-04T21:10:06.9009331Z Running cpp/tensor_iterator_test 1/1 ... [2025-03-04 21:10:06.900487]
2025-03-04T21:10:06.9010270Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:10:06.9014615Z 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-d2f4f6eac064d85e.xml', '-x', '--reruns=2'] ... [2025-03-04 21:10:06.900995]
2025-03-04T21:10:09.1554818Z 
2025-03-04T21:10:09.1556359Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_dc0fa88a0ab28cb5_.log
2025-03-04T21:10:09.1559483Z 
2025-03-04T21:10:09.8160406Z Running cpp/undefined_tensor_test 1/1 ... [2025-03-04 21:10:09.815585]
2025-03-04T21:10:09.8161355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:10:09.8164616Z 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-3d48c2f797749267.xml', '-x', '--reruns=2'] ... [2025-03-04 21:10:09.816073]
2025-03-04T21:10:12.6358748Z 
2025-03-04T21:10:12.6360168Z 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_d004f9ea4a2ec8a1_.log
2025-03-04T21:10:12.6361359Z 
2025-03-04T21:10:13.4500968Z Running cpp/wrapdim_test 1/1 ... [2025-03-04 21:10:13.449695]
2025-03-04T21:10:13.4501763Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2025-03-04T21:10:13.4505952Z 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-eeec955a1057374f.xml', '-x', '--reruns=2'] ... [2025-03-04 21:10:13.450157]
2025-03-04T21:10:16.4204924Z 
2025-03-04T21:10:16.4206350Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_ce05e4358d414bc9_.log
2025-03-04T21:10:16.4208129Z 
2025-03-04T21:10:19.6902557Z 
2025-03-04T21:10:19.6903878Z 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_b3d0a4df216b6386_.log
2025-03-04T21:10:19.6904975Z 
2025-03-04T21:10:20.9223503Z Running test batch 'tests to run' cost 86.22 seconds
2025-03-04T21:10:21.6283611Z + run_if_exists tensor_interop_test
2025-03-04T21:10:21.6284247Z + local test_name=tensor_interop_test
2025-03-04T21:10:21.6284837Z + [[ -x build/bin/tensor_interop_test ]]
2025-03-04T21:10:21.6285480Z + echo 'Warning: tensor_interop_test does not exist.'
2025-03-04T21:10:21.6286138Z Warning: tensor_interop_test does not exist.
2025-03-04T21:10:21.6286695Z + run_if_exists cudnn_test
2025-03-04T21:10:21.6287175Z + local test_name=cudnn_test
2025-03-04T21:10:21.6287709Z + [[ -x build/bin/cudnn_test ]]
2025-03-04T21:10:21.6288291Z + echo 'Warning: cudnn_test does not exist.'
2025-03-04T21:10:21.6288936Z Warning: cudnn_test does not exist.
2025-03-04T21:10:21.6289885Z + run_if_exists cuda_generator_test
2025-03-04T21:10:21.6290417Z + local test_name=cuda_generator_test
2025-03-04T21:10:21.6290824Z + [[ -x build/bin/cuda_generator_test ]]
2025-03-04T21:10:21.6291391Z + echo 'Warning: cuda_generator_test does not exist.'
2025-03-04T21:10:21.6292037Z Warning: cuda_generator_test does not exist.
2025-03-04T21:10:21.6292473Z + run_if_exists apply_test
2025-03-04T21:10:21.6292780Z + local test_name=apply_test
2025-03-04T21:10:21.6293146Z + [[ -x build/bin/apply_test ]]
2025-03-04T21:10:21.6293522Z + echo 'Warning: apply_test does not exist.'
2025-03-04T21:10:21.6293914Z Warning: apply_test does not exist.
2025-03-04T21:10:21.6294286Z + run_if_exists stream_test
2025-03-04T21:10:21.6294596Z + local test_name=stream_test
2025-03-04T21:10:21.6294972Z + [[ -x build/bin/stream_test ]]
2025-03-04T21:10:21.6295315Z + echo 'Warning: stream_test does not exist.'
2025-03-04T21:10:21.6295750Z Warning: stream_test does not exist.
2025-03-04T21:10:21.6296092Z + run_if_exists cuda_half_test
2025-03-04T21:10:21.6296473Z + local test_name=cuda_half_test
2025-03-04T21:10:21.6296801Z + [[ -x build/bin/cuda_half_test ]]
2025-03-04T21:10:21.6297204Z + echo 'Warning: cuda_half_test does not exist.'
2025-03-04T21:10:21.6297591Z Warning: cuda_half_test does not exist.
2025-03-04T21:10:21.6298193Z + run_if_exists cuda_vectorized_test
2025-03-04T21:10:21.6298570Z + local test_name=cuda_vectorized_test
2025-03-04T21:10:21.6298962Z + [[ -x build/bin/cuda_vectorized_test ]]
2025-03-04T21:10:21.6299420Z + echo 'Warning: cuda_vectorized_test does not exist.'
2025-03-04T21:10:21.6299849Z Warning: cuda_vectorized_test does not exist.
2025-03-04T21:10:21.6300288Z + run_if_exists cuda_distributions_test
2025-03-04T21:10:21.6300658Z + local test_name=cuda_distributions_test
2025-03-04T21:10:21.6301091Z + [[ -x build/bin/cuda_distributions_test ]]
2025-03-04T21:10:21.6301520Z + echo 'Warning: cuda_distributions_test does not exist.'
2025-03-04T21:10:21.6302036Z Warning: cuda_distributions_test does not exist.
2025-03-04T21:10:21.6302483Z + run_if_exists cuda_optional_test
2025-03-04T21:10:21.6302828Z + local test_name=cuda_optional_test
2025-03-04T21:10:21.6303226Z + [[ -x build/bin/cuda_optional_test ]]
2025-03-04T21:10:21.6303623Z + echo 'Warning: cuda_optional_test does not exist.'
2025-03-04T21:10:21.6304096Z Warning: cuda_optional_test does not exist.
2025-03-04T21:10:21.6304464Z + run_if_exists cuda_tensor_interop_test
2025-03-04T21:10:21.6304884Z + local test_name=cuda_tensor_interop_test
2025-03-04T21:10:21.6305268Z + [[ -x build/bin/cuda_tensor_interop_test ]]
2025-03-04T21:10:21.6305851Z + echo 'Warning: cuda_tensor_interop_test does not exist.'
2025-03-04T21:10:21.6306364Z Warning: cuda_tensor_interop_test does not exist.
2025-03-04T21:10:21.6306755Z + run_if_exists cuda_complex_test
2025-03-04T21:10:21.6307142Z + local test_name=cuda_complex_test
2025-03-04T21:10:21.6307489Z + [[ -x build/bin/cuda_complex_test ]]
2025-03-04T21:10:21.6308020Z + echo 'Warning: cuda_complex_test does not exist.'
2025-03-04T21:10:21.6308429Z Warning: cuda_complex_test does not exist.
2025-03-04T21:10:21.6308859Z + run_if_exists cuda_complex_math_test
2025-03-04T21:10:21.6309215Z + local test_name=cuda_complex_math_test
2025-03-04T21:10:21.6309638Z + [[ -x build/bin/cuda_complex_math_test ]]
2025-03-04T21:10:21.6310094Z + echo 'Warning: cuda_complex_math_test does not exist.'
2025-03-04T21:10:21.6310539Z Warning: cuda_complex_math_test does not exist.
2025-03-04T21:10:21.6310967Z + run_if_exists cuda_cub_test
2025-03-04T21:10:21.6311282Z + local test_name=cuda_cub_test
2025-03-04T21:10:21.6311652Z + [[ -x build/bin/cuda_cub_test ]]
2025-03-04T21:10:21.6312003Z + echo 'Warning: cuda_cub_test does not exist.'
2025-03-04T21:10:21.6312437Z Warning: cuda_cub_test does not exist.
2025-03-04T21:10:21.6312790Z + run_if_exists cuda_atomic_ops_test
2025-03-04T21:10:21.6313187Z + local test_name=cuda_atomic_ops_test
2025-03-04T21:10:21.6313548Z + [[ -x build/bin/cuda_atomic_ops_test ]]
2025-03-04T21:10:21.6314005Z + echo 'Warning: cuda_atomic_ops_test does not exist.'
2025-03-04T21:10:21.6314476Z Warning: cuda_atomic_ops_test does not exist.
2025-03-04T21:10:21.6314887Z + '[' ON == ON ']'
2025-03-04T21:10:21.6315669Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA'
2025-03-04T21:10:21.6623349Z ==7053== Memcheck, a memory error detector
2025-03-04T21:10:21.6624119Z ==7053== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al.
2025-03-04T21:10:21.6624710Z ==7053== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info
2025-03-04T21:10:21.6625249Z ==7053== Command: build/bin/basic --gtest_filter=-*CUDA
2025-03-04T21:10:21.6625625Z ==7053== 
2025-03-04T21:10:22.1788008Z ==7053== Warning: set address range perms: large range [0x4a08000, 0x15a29000) (defined)
2025-03-04T21:10:50.6965933Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2025-03-04T21:10:50.7243836Z Note: Google Test filter = -*CUDA
2025-03-04T21:10:50.7289703Z [==========] Running 4 tests from 1 test suite.
2025-03-04T21:10:50.7319557Z [----------] Global test environment set-up.
2025-03-04T21:10:50.7390083Z [----------] 4 tests from BasicTest
2025-03-04T21:10:50.7413822Z [ RUN      ] BasicTest.BasicTestCPU
2025-03-04T21:10:52.1605634Z    370 ms
2025-03-04T21:10:52.2454923Z    54 ms
2025-03-04T21:10:52.3189782Z    65 ms
2025-03-04T21:10:52.9627197Z [       OK ] BasicTest.BasicTestCPU (2219 ms)
2025-03-04T21:10:52.9677307Z [ RUN      ] BasicTest.BasicTestHalfCPU
2025-03-04T21:10:53.1148433Z    102 ms
2025-03-04T21:10:53.1655470Z    45 ms
2025-03-04T21:10:53.2314927Z    63 ms
2025-03-04T21:10:53.2849862Z [       OK ] BasicTest.BasicTestHalfCPU (316 ms)
2025-03-04T21:10:53.2850389Z [ RUN      ] BasicTest.FactoryMethodsTest
2025-03-04T21:10:53.3175426Z [       OK ] BasicTest.FactoryMethodsTest (32 ms)
2025-03-04T21:10:53.3175937Z [ RUN      ] BasicTest.BasicStdTestCPU
2025-03-04T21:10:53.4011961Z Simple example: called once
2025-03-04T21:10:53.4926158Z throw: call_once will retry
2025-03-04T21:10:53.5347889Z throw: call_once will retry
2025-03-04T21:10:53.5352029Z Didn't throw, call_once will not attempt again
2025-03-04T21:10:53.5372286Z [       OK ] BasicTest.BasicStdTestCPU (219 ms)
2025-03-04T21:10:53.5396103Z [----------] 4 tests from BasicTest (2796 ms total)
2025-03-04T21:10:53.5396421Z 
2025-03-04T21:10:53.5410309Z [----------] Global test environment tear-down
2025-03-04T21:10:53.5442896Z [==========] 4 tests from 1 test suite ran. (2823 ms total)
2025-03-04T21:10:53.5453926Z [  PASSED  ] 4 tests.
2025-03-04T21:10:55.3401196Z ==7053== 
2025-03-04T21:10:55.3406080Z ==7053== HEAP SUMMARY:
2025-03-04T21:10:55.3406723Z ==7053==     in use at exit: 240,152 bytes in 3,996 blocks
2025-03-04T21:10:55.3407599Z ==7053==   total heap usage: 749,701 allocs, 745,705 frees, 215,475,936 bytes allocated
2025-03-04T21:10:55.3408420Z ==7053== 
2025-03-04T21:10:55.3789106Z ==7053== LEAK SUMMARY:
2025-03-04T21:10:55.3789791Z ==7053==    definitely lost: 0 bytes in 0 blocks
2025-03-04T21:10:55.3790564Z ==7053==    indirectly lost: 0 bytes in 0 blocks
2025-03-04T21:10:55.3791310Z ==7053==      possibly lost: 0 bytes in 0 blocks
2025-03-04T21:10:55.3792089Z ==7053==    still reachable: 240,152 bytes in 3,996 blocks
2025-03-04T21:10:55.3792885Z ==7053==         suppressed: 0 bytes in 0 blocks
2025-03-04T21:10:55.3793774Z ==7053== Rerun with --leak-check=full to see details of leaked memory
2025-03-04T21:10:55.3794602Z ==7053== 
2025-03-04T21:10:55.3795245Z ==7053== For lists of detected and suppressed errors, rerun with: -s
2025-03-04T21:10:55.3796329Z ==7053== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
2025-03-04T21:10:55.4256519Z + [[ -x build/bin/tensor_interop_test ]]
2025-03-04T21:10:55.4258226Z + [[ -n '' ]]
2025-03-04T21:10:55.4258577Z + assert_git_not_dirty
2025-03-04T21:10:55.4258901Z + [[ linux-focal-py3.13-clang10 != *rocm* ]]
2025-03-04T21:10:55.4259316Z + [[ linux-focal-py3.13-clang10 != *xla* ]]
2025-03-04T21:10:55.4265520Z ++ git status --porcelain
2025-03-04T21:10:55.4266478Z ++ grep -v '?? third_party'
2025-03-04T21:10:55.6537931Z ++ true
2025-03-04T21:10:55.6538661Z + git_status=
2025-03-04T21:10:55.6538956Z + [[ -n '' ]]
2025-03-04T21:10:55.6589589Z + cleanup_workspace
2025-03-04T21:10:55.6590570Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.'
2025-03-04T21:10:55.6592057Z sudo may print the following warning message that can be ignored. The chown command will still run.
2025-03-04T21:10:55.6593026Z + echo '    sudo: setrlimit(RLIMIT_STACK): Operation not permitted'
2025-03-04T21:10:55.6593547Z     sudo: setrlimit(RLIMIT_STACK): Operation not permitted
2025-03-04T21:10:55.6594123Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42'
2025-03-04T21:10:55.6594746Z For more details refer to https://github.com/sudo-project/sudo/issues/42
2025-03-04T21:10:55.6595265Z + sudo chown -R 1000 /var/lib/jenkins/workspace
2025-03-04T21:10:58.1340636Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main
2025-03-04T21:10:58.1341151Z with:
2025-03-04T21:10:58.1341428Z   benchmark-results-dir: test/test-reports
2025-03-04T21:10:58.1341786Z   dry-run: false
2025-03-04T21:10:58.1342146Z   schema-version: v3
2025-03-04T21:10:58.1342627Z   github-token: ***
2025-03-04T21:10:58.1342886Z env:
2025-03-04T21:10:58.1343129Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:10:58.1343631Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:10:58.1344168Z ##[endgroup]
2025-03-04T21:10:58.1381244Z ##[group]Run set -eux
2025-03-04T21:10:58.1381552Z set -eux
2025-03-04T21:10:58.1381853Z python3 -mpip install boto3==1.35.33
2025-03-04T21:10:58.1429029Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:10:58.1429451Z env:
2025-03-04T21:10:58.1429700Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:10:58.1430260Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:10:58.1430783Z ##[endgroup]
2025-03-04T21:10:58.1461036Z + python3 -mpip install boto3==1.35.33
2025-03-04T21:10:58.4776526Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T21:10:59.5421811Z Collecting boto3==1.35.33
2025-03-04T21:10:59.5639898Z   Downloading boto3-1.35.33-py3-none-any.whl (139 kB)
2025-03-04T21:10:59.6224785Z Collecting s3transfer<0.11.0,>=0.10.0
2025-03-04T21:10:59.6258730Z   Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB)
2025-03-04T21:10:59.6315618Z 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)
2025-03-04T21:11:00.8123086Z Collecting botocore<1.36.0,>=1.35.33
2025-03-04T21:11:00.8158127Z   Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB)
2025-03-04T21:11:00.9789331Z 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)
2025-03-04T21:11:00.9799514Z 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)
2025-03-04T21:11:01.2194013Z 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)
2025-03-04T21:11:01.3179395Z Installing collected packages: botocore, s3transfer, boto3
2025-03-04T21:11:01.8729274Z Successfully installed boto3-1.35.33 botocore-1.35.99 s3transfer-0.10.4
2025-03-04T21:11:01.9952447Z ##[group]Run set -eux
2025-03-04T21:11:01.9952948Z set -eux
2025-03-04T21:11:01.9953389Z 
2025-03-04T21:11:01.9953821Z if [[ -z "${GITHUB_TOKEN}" ]]; then
2025-03-04T21:11:01.9954451Z   echo "Missing github-token input"
2025-03-04T21:11:01.9955017Z   exit 1
2025-03-04T21:11:01.9955411Z fi
2025-03-04T21:11:01.9965637Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:01.9966291Z env:
2025-03-04T21:11:01.9966663Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:01.9967449Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:01.9968443Z   GITHUB_TOKEN: ***
2025-03-04T21:11:01.9968798Z ##[endgroup]
2025-03-04T21:11:01.9998966Z + [[ -z *** ]]
2025-03-04T21:11:02.0060929Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main
2025-03-04T21:11:02.0061403Z with:
2025-03-04T21:11:02.0061901Z   github-token: ***
2025-03-04T21:11:02.0062168Z env:
2025-03-04T21:11:02.0062415Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:02.0063061Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:02.0077394Z ##[endgroup]
2025-03-04T21:11:02.0098525Z ##[group]Run set -eux
2025-03-04T21:11:02.0098819Z set -eux
2025-03-04T21:11:02.0099071Z 
2025-03-04T21:11:02.0099759Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-04T21:11:02.0105398Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:02.0105805Z env:
2025-03-04T21:11:02.0106054Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:02.0106557Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:02.0107433Z   GITHUB_TOKEN: ***
2025-03-04T21:11:02.0107703Z ##[endgroup]
2025-03-04T21:11:02.0131153Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13661694839 i-0f1aad72ac3d41ea9
2025-03-04T21:11:04.6606807Z setting job-id=38194769830
2025-03-04T21:11:04.6607410Z setting job-name=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T21:11:04.6718630Z ##[group]Run set -eux
2025-03-04T21:11:04.6718959Z set -eux
2025-03-04T21:11:04.6719218Z 
2025-03-04T21:11:04.6719656Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \
2025-03-04T21:11:04.6720454Z   --schema-version "${SCHEMA_VERSION}" \
2025-03-04T21:11:04.6720842Z   --repo "${REPO}" \
2025-03-04T21:11:04.6721181Z   --head-branch "${HEAD_BRANCH}" \
2025-03-04T21:11:04.6721564Z   --head-sha "${HEAD_SHA}" \
2025-03-04T21:11:04.6721935Z   --workflow-id "${WORKFLOW_RUN_ID}" \
2025-03-04T21:11:04.6722327Z   --run-attempt "${RUN_ATTEMPT}" \
2025-03-04T21:11:04.6722687Z   --job-id "${JOB_ID}" \
2025-03-04T21:11:04.6723016Z   --job-name "${JOB_NAME}"
2025-03-04T21:11:04.6730248Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:04.6730662Z env:
2025-03-04T21:11:04.6730895Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:04.6731398Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:04.6732057Z   SCHEMA_VERSION: v3
2025-03-04T21:11:04.6732356Z   REPO: pytorch/pytorch
2025-03-04T21:11:04.6732659Z   HEAD_BRANCH: gh/williamwen42/215/head
2025-03-04T21:11:04.6733045Z   HEAD_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:11:04.6733426Z   WORKFLOW_RUN_ID: 13661694839
2025-03-04T21:11:04.6733743Z   RUN_ATTEMPT: 1
2025-03-04T21:11:04.6734006Z   JOB_ID: 38194769830
2025-03-04T21:11:04.6734464Z   JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T21:11:04.6734977Z ##[endgroup]
2025-03-04T21:11:04.6762298Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch gh/williamwen42/215/head --head-sha 1b7498080987913ecb3aff6253c5e88f3540d911 --workflow-id 13661694839 --run-attempt 1 --job-id 38194769830 --job-name 'linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)'
2025-03-04T21:11:04.7076615Z ##[group]Run set -eux
2025-03-04T21:11:04.7076940Z set -eux
2025-03-04T21:11:04.7077195Z 
2025-03-04T21:11:04.7077472Z # TODO (huydhn): Implement this part
2025-03-04T21:11:04.7077873Z echo "runners=[]" >> "${GITHUB_OUTPUT}"
2025-03-04T21:11:04.7083505Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:04.7083927Z env:
2025-03-04T21:11:04.7084161Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:04.7084657Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:04.7085189Z ##[endgroup]
2025-03-04T21:11:04.7107808Z + echo 'runners=[]'
2025-03-04T21:11:04.7132513Z ##[group]Run set -eux
2025-03-04T21:11:04.7132812Z set -eux
2025-03-04T21:11:04.7133068Z 
2025-03-04T21:11:04.7133345Z # TODO (huydhn): Implement this part
2025-03-04T21:11:04.7133763Z echo "dependencies={}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:11:04.7139259Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:04.7139846Z env:
2025-03-04T21:11:04.7140099Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:04.7140606Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:04.7141152Z ##[endgroup]
2025-03-04T21:11:04.7162992Z + echo 'dependencies={}'
2025-03-04T21:11:04.7199222Z ##[group]Run set -eux
2025-03-04T21:11:04.7199742Z set -eux
2025-03-04T21:11:04.7200168Z 
2025-03-04T21:11:04.7200644Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then
2025-03-04T21:11:04.7201455Z   echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping"
2025-03-04T21:11:04.7202301Z   # We don't want the job to fail if the directory doesn't exist
2025-03-04T21:11:04.7202991Z   exit 0
2025-03-04T21:11:04.7203412Z fi
2025-03-04T21:11:04.7203820Z 
2025-03-04T21:11:04.7204251Z if [[ "${DRY_RUN}" == "true" ]]; then
2025-03-04T21:11:04.7205179Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-04T21:11:04.7206238Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-04T21:11:04.7207050Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-04T21:11:04.7207661Z     --runners "${RUNNER_INFO}" \
2025-03-04T21:11:04.7208346Z     --dependencies "${DEPENDENCIES}" \
2025-03-04T21:11:04.7208966Z     --dry-run
2025-03-04T21:11:04.7209449Z else
2025-03-04T21:11:04.7210153Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-04T21:11:04.7211098Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-04T21:11:04.7211834Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-04T21:11:04.7212451Z     --runners "${RUNNER_INFO}" \
2025-03-04T21:11:04.7213084Z     --dependencies "${DEPENDENCIES}"
2025-03-04T21:11:04.7213655Z fi
2025-03-04T21:11:04.7221540Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:04.7222249Z env:
2025-03-04T21:11:04.7222640Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:04.7223495Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:04.7224511Z   BENCHMARK_RESULTS_DIR: test/test-reports
2025-03-04T21:11:04.7225111Z   DRY_RUN: false
2025-03-04T21:11:04.7227631Z   BENCHMARK_METADATA: {"timestamp": 1741122664, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "gh/williamwen42/215/head", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661694839, "run_attempt": 1, "job_id": 38194769830}
2025-03-04T21:11:04.7230320Z   RUNNER_INFO: []
2025-03-04T21:11:04.7230787Z   DEPENDENCIES: {}
2025-03-04T21:11:04.7231247Z ##[endgroup]
2025-03-04T21:11:04.7259064Z + [[ ! -d test/test-reports ]]
2025-03-04T21:11:04.7259633Z + [[ false == \t\r\u\e ]]
2025-03-04T21:11:04.7264041Z + 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": 1741122664, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "gh/williamwen42/215/head", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661694839, "run_attempt": 1, "job_id": 38194769830}' --runners '[]' --dependencies '{}'
2025-03-04T21:11:04.9570795Z ##[group]Run cat test/**/*_toprint.log || true
2025-03-04T21:11:04.9571222Z cat test/**/*_toprint.log || true
2025-03-04T21:11:04.9577264Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:04.9577682Z env:
2025-03-04T21:11:04.9578007Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:04.9578509Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:04.9579043Z ##[endgroup]
2025-03-04T21:11:04.9649039Z cat: 'test/**/*_toprint.log': No such file or directory
2025-03-04T21:11:04.9681594Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2025-03-04T21:11:04.9681977Z kill "$MONITOR_SCRIPT_PID"
2025-03-04T21:11:04.9687393Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:04.9687805Z env:
2025-03-04T21:11:04.9688050Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:04.9688536Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:04.9689125Z   MONITOR_SCRIPT_PID: 40800
2025-03-04T21:11:04.9689462Z ##[endgroup]
2025-03-04T21:11:04.9837719Z Prepare all required actions
2025-03-04T21:11:04.9838234Z Getting action download info
2025-03-04T21:11:05.1486551Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1)
2025-03-04T21:11:05.5749914Z ##[group]Run ./.github/actions/upload-test-artifacts
2025-03-04T21:11:05.5750603Z with:
2025-03-04T21:11:05.5751228Z   file-suffix: test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T21:11:05.5752034Z   s3-bucket: gha-artifacts
2025-03-04T21:11:05.5752528Z env:
2025-03-04T21:11:05.5752949Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:05.5753863Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:05.5754877Z ##[endgroup]
2025-03-04T21:11:05.5794925Z ##[group]Run # Remove any previous test jsons if they exist
2025-03-04T21:11:05.5795423Z # Remove any previous test jsons if they exist
2025-03-04T21:11:05.5795830Z rm -f test-jsons-*.zip
2025-03-04T21:11:05.5796339Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json'
2025-03-04T21:11:05.5802064Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:05.5802473Z env:
2025-03-04T21:11:05.5802719Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:05.5803320Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:05.5803978Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T21:11:05.5804421Z ##[endgroup]
2025-03-04T21:11:05.5930180Z   adding: test/test-reports/td_exclusions-59d1e1946162857254a3.json (deflated 81%)
2025-03-04T21:11:05.5930915Z   adding: test/test-reports/td_exclusions-ec31d46bb7d932cd3a2c.json (deflated 73%)
2025-03-04T21:11:05.5958928Z ##[group]Run # Remove any previous test reports if they exist
2025-03-04T21:11:05.5959441Z # Remove any previous test reports if they exist
2025-03-04T21:11:05.5959860Z rm -f test-reports-*.zip
2025-03-04T21:11:05.5960386Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv'
2025-03-04T21:11:05.5965918Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:05.5966329Z env:
2025-03-04T21:11:05.5966576Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:05.5967088Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:05.5967741Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T21:11:05.5968177Z ##[endgroup]
2025-03-04T21:11:05.6066446Z   adding: test/test-reports/python-pytest/test_torch/test_torch-09d09b4be1d59a7c.xml (deflated 95%)
2025-03-04T21:11:05.6096374Z   adding: test/test-reports/python-pytest/test_nn/test_nn-ca3a83e0b2bb1317.xml (deflated 96%)
2025-03-04T21:11:05.6099604Z   adding: test/test-reports/python-pytest/test_cpp_extensions_open_device_registration/test_cpp_extensions_open_device_registration-6c84720968c03963.xml (deflated 91%)
2025-03-04T21:11:05.6212351Z   adding: test/test-reports/python-pytest/test_utils/test_utils-b15feb7f99a9606d.xml (deflated 99%)
2025-03-04T21:11:05.6219820Z   adding: test/test-reports/python-pytest/test_fake_tensor/test_fake_tensor-ebe00613908052ee.xml (deflated 94%)
2025-03-04T21:11:05.6220775Z   adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-b050e3ade578c048.xml (deflated 37%)
2025-03-04T21:11:05.6224016Z   adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-727d28aabda450d1.xml (deflated 95%)
2025-03-04T21:11:05.6225096Z   adding: test/test-reports/python-pytest/test_dispatch/test_dispatch-61c3781846188b79.xml (deflated 76%)
2025-03-04T21:11:05.6226223Z   adding: test/test-reports/python-pytest/test_autocast/test_autocast-e889fef879251bc7.xml (deflated 86%)
2025-03-04T21:11:05.6244438Z   adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-df32d41ec4cfcb2c.xml (deflated 94%)
2025-03-04T21:11:05.6247454Z   adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-8e6573fd90329520.xml (deflated 90%)
2025-03-04T21:11:05.6248977Z   adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-dbae275cda5a953d.xml (deflated 95%)
2025-03-04T21:11:05.6266397Z   adding: test/test-reports/python-pytest/nn.test_convolution/nn.test_convolution-43fcdb8f715a901c.xml (deflated 97%)
2025-03-04T21:11:05.6269050Z   adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-aaacc5dc838bb640.xml (deflated 90%)
2025-03-04T21:11:05.6271037Z   adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-f307511f5a2cbc80.xml (deflated 94%)
2025-03-04T21:11:05.6273281Z   adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-f3dcfa0b4513ca64.xml (deflated 89%)
2025-03-04T21:11:05.6274514Z   adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-a2e1a0f0ba1c2e5b.xml (deflated 58%)
2025-03-04T21:11:05.6304522Z   adding: test/test-reports/python-pytest/test_fx/test_fx-7e46696743663c35.xml (deflated 96%)
2025-03-04T21:11:05.6312202Z   adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-1bea31b70b2dd1fb.xml (deflated 95%)
2025-03-04T21:11:05.6317702Z   adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-0401a1a05e34bef6.xml (deflated 94%)
2025-03-04T21:11:05.6337338Z   adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-d4023fb08288ae27.xml (deflated 96%)
2025-03-04T21:11:05.6352970Z   adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-f0b0d607c81b4cba.xml (deflated 96%)
2025-03-04T21:11:05.6354292Z   adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-96c91a56ab30ef58.xml (deflated 95%)
2025-03-04T21:11:05.6355515Z   adding: test/test-reports/python-pytest/test_namedtuple_return_api/test_namedtuple_return_api-67ad49bd33c181ad.xml (deflated 72%)
2025-03-04T21:11:05.6356686Z   adding: test/test-reports/python-pytest/test_autograd_fallback/test_autograd_fallback-7b22fb51fcb2d5f1.xml (deflated 88%)
2025-03-04T21:11:05.6357782Z   adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-7b8f69949dd20a98.xml (deflated 56%)
2025-03-04T21:11:05.6358968Z   adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-b6ed0cab2d231725.xml (deflated 90%)
2025-03-04T21:11:05.6360169Z   adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-b70dff3c97226951.xml (deflated 90%)
2025-03-04T21:11:05.6457748Z   adding: test/test-reports/python-pytest/test_reductions/test_reductions-bca9ee7d1796a59e.xml (deflated 98%)
2025-03-04T21:11:05.6482240Z   adding: test/test-reports/python-pytest/test_overrides/test_overrides-3e78fb0b19786841.xml (deflated 96%)
2025-03-04T21:11:05.6483544Z   adding: test/test-reports/python-pytest/test_transformers_privateuse1/test_transformers_privateuse1-d2c4634e607b0d76.xml (deflated 71%)
2025-03-04T21:11:05.6484814Z   adding: test/test-reports/python-pytest/test_extension_utils/test_extension_utils-419defe7a180d283.xml (deflated 51%)
2025-03-04T21:11:05.6485946Z   adding: test/test-reports/python-pytest/test_cpp_extensions_mtia_backend/test_cpp_extensions_mtia_backend-91b2cfbf974f2157.xml (deflated 79%)
2025-03-04T21:11:05.6487206Z   adding: test/test-reports/python-pytest/test_cpp_extensions_stream_and_event/test_cpp_extensions_stream_and_event-aa9e5581c16a914e.xml (deflated 59%)
2025-03-04T21:11:05.6488309Z   adding: test/test-reports/python-pytest/test_jiterator/test_jiterator-3e32b97f6b6dbb60.xml (deflated 28%)
2025-03-04T21:11:05.6489382Z   adding: test/test-reports/python-pytest/test_jiterator/test_jiterator-4e9d5f14658494a2.xml (deflated 28%)
2025-03-04T21:11:05.6490307Z   adding: test/test-reports/python-pytest/test_jit_fuser_te/test_jit_fuser_te-c04b162785c597c9.xml (deflated 28%)
2025-03-04T21:11:05.6618457Z   adding: test/test-reports/python-pytest/test_jit_fuser_te/test_jit_fuser_te-27ae869588587436.xml (deflated 98%)
2025-03-04T21:11:05.6619590Z   adding: test/test-reports/python-pytest/test_accelerator/test_accelerator-309c32e156c13154.xml (deflated 28%)
2025-03-04T21:11:05.6620679Z   adding: test/test-reports/python-pytest/test_accelerator/test_accelerator-f83689ff5b57bdf8.xml (deflated 28%)
2025-03-04T21:11:05.6621735Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-7607452af12c0391.xml (deflated 29%)
2025-03-04T21:11:05.6622757Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d212854796f313c3.xml (deflated 29%)
2025-03-04T21:11:05.6623746Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-3b38a93f9bc07a26.xml (deflated 29%)
2025-03-04T21:11:05.6624710Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1b43d71462888da1.xml (deflated 29%)
2025-03-04T21:11:05.6625683Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-9dd267e7195e4e5e.xml (deflated 29%)
2025-03-04T21:11:05.6626668Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-36bd3185c417c048.xml (deflated 28%)
2025-03-04T21:11:05.6627624Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-8984e353ecae438b.xml (deflated 29%)
2025-03-04T21:11:05.6628617Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1c2499c7e2fb650e.xml (deflated 29%)
2025-03-04T21:11:05.6629678Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-333a13429908a6c7.xml (deflated 28%)
2025-03-04T21:11:05.6630678Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-37a18bd3bb3a37e1.xml (deflated 29%)
2025-03-04T21:11:05.6631640Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-75685ade51c384f5.xml (deflated 29%)
2025-03-04T21:11:05.6632609Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1bfe850fd0fd24a1.xml (deflated 29%)
2025-03-04T21:11:05.6633534Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-6f92e162ef9253ff.xml (deflated 29%)
2025-03-04T21:11:05.6634413Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-c3194177e01aecd3.xml (deflated 29%)
2025-03-04T21:11:05.6635290Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-27de0bbbc5843243.xml (deflated 29%)
2025-03-04T21:11:05.6636169Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-fa4813567ed62aaf.xml (deflated 29%)
2025-03-04T21:11:05.6637401Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-a071992cc7e449c9.xml (deflated 29%)
2025-03-04T21:11:05.6638885Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-78803bcf306b0db0.xml (deflated 29%)
2025-03-04T21:11:05.6639773Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-54f9ddd3526b816a.xml (deflated 29%)
2025-03-04T21:11:05.6640646Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-17d17ca2c3974e91.xml (deflated 57%)
2025-03-04T21:11:05.6641543Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-e67f2393538f0c78.xml (deflated 73%)
2025-03-04T21:11:05.6642450Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-3eee5484414e0482.xml (deflated 83%)
2025-03-04T21:11:05.6643339Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-e1fe8bc1d16ce9d0.xml (deflated 66%)
2025-03-04T21:11:05.6644229Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-2a8f349f79f3bcb3.xml (deflated 78%)
2025-03-04T21:11:05.6645116Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-6adeb1b30f588ec4.xml (deflated 37%)
2025-03-04T21:11:05.6646086Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-97cb607fe29021bd.xml (deflated 61%)
2025-03-04T21:11:05.6646970Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-cd9b59b20f551e9a.xml (deflated 80%)
2025-03-04T21:11:05.6647934Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-6f849a33d9deb5a3.xml (deflated 51%)
2025-03-04T21:11:05.6648819Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-b7668fde21ff4ab2.xml (deflated 35%)
2025-03-04T21:11:05.6649692Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-480ac9f6b6381436.xml (deflated 46%)
2025-03-04T21:11:05.6650576Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-92a275b927f7b0b4.xml (deflated 47%)
2025-03-04T21:11:05.6651463Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-68421525c917a134.xml (deflated 83%)
2025-03-04T21:11:05.6652347Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-2f06fa238042999b.xml (deflated 58%)
2025-03-04T21:11:05.6653230Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-57dc20e4ebef243d.xml (deflated 57%)
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2025-03-04T21:11:05.6654996Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-3d48c2f797749267.xml (deflated 37%)
2025-03-04T21:11:05.6655871Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-eeec955a1057374f.xml (deflated 37%)
2025-03-04T21:11:05.6656753Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-d2f4f6eac064d85e.xml (deflated 90%)
2025-03-04T21:11:05.6657859Z   adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20250304205919.xml (deflated 43%)
2025-03-04T21:11:05.6658962Z   adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20250304205930.xml (deflated 43%)
2025-03-04T21:11:05.6687679Z ##[group]Run # Remove any previous usage logs if they exist
2025-03-04T21:11:05.6688182Z # Remove any previous usage logs if they exist
2025-03-04T21:11:05.6688583Z rm -f logs-*.zip
2025-03-04T21:11:05.6689083Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2025-03-04T21:11:05.6689674Z # so check to see if the file exists first
2025-03-04T21:11:05.6690062Z if [ -f 'usage_log.txt' ]; then
2025-03-04T21:11:05.6690462Z     zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt'
2025-03-04T21:11:05.6690836Z fi
2025-03-04T21:11:05.6691235Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then
2025-03-04T21:11:05.6691824Z     zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log'
2025-03-04T21:11:05.6692248Z fi
2025-03-04T21:11:05.6697859Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:05.6698282Z env:
2025-03-04T21:11:05.6698531Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:05.6699034Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:05.6699688Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T21:11:05.6700124Z ##[endgroup]
2025-03-04T21:11:05.6780320Z   adding: usage_log.txt (deflated 96%)
2025-03-04T21:11:05.6908722Z   adding: test/test-reports/test_torch_1.1_7f8c4481062be502_.log (deflated 92%)
2025-03-04T21:11:05.6942351Z   adding: test/test-reports/test_nn_2.2_d0496998163d97f2_.log (deflated 94%)
2025-03-04T21:11:05.6943996Z   adding: test/test-reports/test_cpp_extensions_open_device_registration_1.1_368e3adba1fdfc66_.log (deflated 78%)
2025-03-04T21:11:05.7053679Z   adding: test/test-reports/test_utils_1.1_53a280d80a47fccb_.log (deflated 96%)
2025-03-04T21:11:05.7061395Z   adding: test/test-reports/test_fake_tensor_1.1_6c8859ddde3d10ac_.log (deflated 91%)
2025-03-04T21:11:05.7062175Z   adding: test/test-reports/test_show_pickle_1.1_da5dda921c105935_.log (deflated 51%)
2025-03-04T21:11:05.7064452Z   adding: test/test-reports/test_multiprocessing_1.1_a6209014cfc7f374_.log (deflated 87%)
2025-03-04T21:11:05.7066431Z   adding: test/test-reports/test_dispatch_1.1_0677422d8715a6d8_.log (deflated 83%)
2025-03-04T21:11:05.7067415Z   adding: test/test-reports/test_autocast_1.1_f5f882e2d87b9790_.log (deflated 77%)
2025-03-04T21:11:05.7089917Z   adding: test/test-reports/test_tensor_creation_ops_1.1_bf1f9f481cfa9b6c_.log (deflated 93%)
2025-03-04T21:11:05.7092237Z   adding: test/test-reports/test_cpp_extensions_jit_1.1_4c48089d03fe3b89_.log (deflated 87%)
2025-03-04T21:11:05.7093433Z   adding: test/test-reports/test_native_mha_1.1_4f85d64063ee6d76_.log (deflated 90%)
2025-03-04T21:11:05.7109059Z   adding: test/test-reports/nn.test_convolution_1.1_a58d9e000db84c83_.log (deflated 95%)
2025-03-04T21:11:05.7116026Z   adding: test/test-reports/test_sort_and_select_1.1_b77f713deefffcc0_.log (deflated 93%)
2025-03-04T21:11:05.7117210Z   adding: test/test-reports/test_multiprocessing_spawn_1.1_670c320406c3949f_.log (deflated 82%)
2025-03-04T21:11:05.7121977Z   adding: test/test-reports/nn.test_pooling_1.1_c57660566d7072b3_.log (deflated 87%)
2025-03-04T21:11:05.7122865Z   adding: test/test-reports/test_mobile_optimizer_1.1_ce88d1d82250f324_.log (deflated 75%)
2025-03-04T21:11:05.7153457Z   adding: test/test-reports/test_fx_1.1_98a6977ddc6c4c72_.log (deflated 92%)
2025-03-04T21:11:05.7161005Z   adding: test/test-reports/test_spectral_ops_1.1_d012086a966f0ae3_.log (deflated 92%)
2025-03-04T21:11:05.7164979Z   adding: test/test-reports/test_python_dispatch_1.1_517b036717830741_.log (deflated 85%)
2025-03-04T21:11:05.7168680Z   adding: test/test-reports/distributions.test_distributions_1.2_9d863c3aea73c2e0_.log (deflated 90%)
2025-03-04T21:11:05.7171630Z   adding: test/test-reports/distributions.test_distributions_2.2_2e4d1e983b5f5d46_.log (deflated 89%)
2025-03-04T21:11:05.7173125Z   adding: test/test-reports/test_tensorexpr_1.1_92b1692a81dba73f_.log (deflated 89%)
2025-03-04T21:11:05.7174115Z   adding: test/test-reports/test_namedtuple_return_api_1.1_250cd7dc33aa8298_.log (deflated 64%)
2025-03-04T21:11:05.7174955Z   adding: test/test-reports/test_autograd_fallback_1.1_3551f7f7147ae718_.log (deflated 84%)
2025-03-04T21:11:05.7175755Z   adding: test/test-reports/test_jit_disabled_1.1_d0da3c03a68982e7_.log (deflated 57%)
2025-03-04T21:11:05.7176603Z   adding: test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_d678bd4c3fd820d4_.log (deflated 77%)
2025-03-04T21:11:05.7177601Z   adding: test/test-reports/test_cpp_extensions_aot_ninja_1.1_be605a22e147c789_.log (deflated 76%)
2025-03-04T21:11:05.7178418Z   adding: test/test-reports/test_cuda_trace_1.1_87971e0343fda7c0_.log (deflated 11%)
2025-03-04T21:11:05.7179126Z   adding: test/test-reports/test_cuda_primary_ctx_1.1_0057f4cee823fd7e_.log (deflated 11%)
2025-03-04T21:11:05.7267807Z   adding: test/test-reports/test_reductions_1.1_baa43e515275452f_.log (deflated 96%)
2025-03-04T21:11:05.7268572Z   adding: test/test-reports/test_cuda_nvml_based_avail_1.1_ab3cf9a3f1e38fc7_.log (deflated 11%)
2025-03-04T21:11:05.7295560Z   adding: test/test-reports/test_overrides_1.1_1b4bed757a5c9c35_.log (deflated 92%)
2025-03-04T21:11:05.7296475Z   adding: test/test-reports/test_transformers_privateuse1_1.1_533f1e2fa652d6fb_.log (deflated 65%)
2025-03-04T21:11:05.7297327Z   adding: test/test-reports/test_extension_utils_1.1_910d2c7af651b230_.log (deflated 57%)
2025-03-04T21:11:05.7298332Z   adding: test/test-reports/test_cpp_extensions_mtia_backend_1.1_c9ed471cad2a366f_.log (deflated 68%)
2025-03-04T21:11:05.7299525Z   adding: test/test-reports/test_cpp_extensions_stream_and_event_1.1_e5bcde3bef77b45a_.log (deflated 59%)
2025-03-04T21:11:05.7300663Z   adding: test/test-reports/dynamo.test_graph_break_messages_1.1_1f34828315ffbbe3_.log (stored 0%)
2025-03-04T21:11:05.7301654Z   adding: test/test-reports/dynamo.test_export_1.1_7b93e16fc1bf0e48_.log (stored 0%)
2025-03-04T21:11:05.7302443Z   adding: test/test-reports/dynamo.test_repros_1.1_bbceb1007cb10153_.log (stored 0%)
2025-03-04T21:11:05.7303379Z   adding: test/test-reports/dynamo.test_decorators_1.1_74fbba70258671bc_.log (stored 0%)
2025-03-04T21:11:05.7304181Z   adding: test/test-reports/dynamo.test_optimizers_1.1_864f46f19a8386ca_.log (stored 0%)
2025-03-04T21:11:05.7305029Z   adding: test/test-reports/dynamo.test_minifier_1.1_b41724883f970a49_.log (stored 0%)
2025-03-04T21:11:05.7305818Z   adding: test/test-reports/dynamo.test_backends_1.1_2cf2f33105bd837e_.log (stored 0%)
2025-03-04T21:11:05.7306665Z   adding: test/test-reports/dynamo.test_aot_autograd_1.1_2a8d4b4c9a3818a5_.log (stored 0%)
2025-03-04T21:11:05.7307480Z   adding: test/test-reports/dynamo.test_functions_1.1_0c8c20fa6289bc21_.log (stored 0%)
2025-03-04T21:11:05.7308351Z   adding: test/test-reports/dynamo.test_skip_non_tensor_1.1_f17869b3afac6ac8_.log (stored 0%)
2025-03-04T21:11:05.7309134Z   adding: test/test-reports/dynamo.test_pre_dispatch_1.1_e567909049f33bf0_.log (stored 0%)
2025-03-04T21:11:05.7310026Z   adding: test/test-reports/dynamo.test_python_autograd_1.1_3138967e066acfde_.log (stored 0%)
2025-03-04T21:11:05.7310935Z   adding: test/test-reports/dynamo.test_exceptions_1.1_5af685361ff1b9ca_.log (stored 0%)
2025-03-04T21:11:05.7311675Z   adding: test/test-reports/dynamo.test_hooks_1.1_9f96a76c48d863cb_.log (stored 0%)
2025-03-04T21:11:05.7312644Z   adding: test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_a2a0a91c41dc7018_.log (stored 0%)
2025-03-04T21:11:05.7313571Z   adding: test/test-reports/dynamo.test_base_output_1.1_d623c79781ed36b7_.log (stored 0%)
2025-03-04T21:11:05.7314392Z   adding: test/test-reports/dynamo.test_reconstruct_1.1_43cab3740971c523_.log (stored 0%)
2025-03-04T21:11:05.7315303Z   adding: test/test-reports/dynamo.test_view_1.1_de0891d33f83d8d3_.log (stored 0%)
2025-03-04T21:11:05.7316045Z   adding: test/test-reports/dynamo.test_trace_rules_1.1_e2e95bb59405ec18_.log (stored 0%)
2025-03-04T21:11:05.7316902Z   adding: test/test-reports/dynamo.test_compile_1.1_2ab09fd9fb1ec736_.log (stored 0%)
2025-03-04T21:11:05.7317681Z   adding: test/test-reports/dynamo.test_deviceguard_1.1_44e05eb4640e09a6_.log (stored 0%)
2025-03-04T21:11:05.7318479Z   adding: test/test-reports/dynamo.test_backward_higher_order_ops_1.1_eefcfbc4e4f72a04_.log (stored 0%)
2025-03-04T21:11:05.7319398Z   adding: test/test-reports/dynamo.test_base_hop_1.1_c8ed99c35676a224_.log (stored 0%)
2025-03-04T21:11:05.7320183Z   adding: test/test-reports/dynamo.test_bytecode_utils_1.1_c714123fd4ae37a4_.log (stored 0%)
2025-03-04T21:11:05.7321158Z   adding: test/test-reports/dynamo.test_aot_autograd_cache_1.1_cc55ba2dd5589533_.log (stored 0%)
2025-03-04T21:11:05.7322017Z   adding: test/test-reports/dynamo.test_input_attr_tracking_1.1_4c27fecbdc9abc30_.log (stored 0%)
2025-03-04T21:11:05.7322799Z   adding: test/test-reports/test_jiterator_1.1_c2ae29113bff11cf_.log (deflated 48%)
2025-03-04T21:11:05.7323484Z   adding: test/test-reports/test_jit_fuser_te_1.1_97d1b8c572d9a18c_.log (deflated 49%)
2025-03-04T21:11:05.7324225Z   adding: test/test-reports/test_appending_byte_serializer_1.1_00b6a36fb47508da_.log (stored 0%)
2025-03-04T21:11:05.7324959Z   adding: test/test-reports/functorch.test_ac_1.1_c3558f7cbfb72469_.log (stored 0%)
2025-03-04T21:11:05.7325650Z   adding: test/test-reports/test_accelerator_1.1_56b7a7bb4ba88bbb_.log (deflated 49%)
2025-03-04T21:11:05.7326348Z   adding: test/test-reports/optim.test_optim_1.1_c5143e1aedb8a052_.log (deflated 7%)
2025-03-04T21:11:05.7327050Z   adding: test/test-reports/cpp.Dict_test_1.1_451119c32606459c_.log (deflated 83%)
2025-03-04T21:11:05.7327744Z   adding: test/test-reports/cpp.legacy_vmap_test_1.1_42cbb8adfd2ff7f9_.log (deflated 80%)
2025-03-04T21:11:05.7328514Z   adding: test/test-reports/dynamo.test_graph_break_messages_1.1_a54d6f8fcb5ef925_.log (stored 0%)
2025-03-04T21:11:05.7329258Z   adding: test/test-reports/dynamo.test_export_1.1_d87a064e32b04763_.log (stored 0%)
2025-03-04T21:11:05.7329941Z   adding: test/test-reports/dynamo.test_repros_1.1_40223a5c24842b68_.log (stored 0%)
2025-03-04T21:11:05.7330712Z   adding: test/test-reports/cpp.NamedTensor_test_1.1_f4fe54826e1a19aa_.log (deflated 71%)
2025-03-04T21:11:05.7331539Z   adding: test/test-reports/cpp.scalar_tensor_test_1.1_56bfd3ec51b9bbee_.log (deflated 60%)
2025-03-04T21:11:05.7332359Z   adding: test/test-reports/dynamo.test_optimizers_1.1_6707d74aa299c478_.log (stored 0%)
2025-03-04T21:11:05.7333074Z   adding: test/test-reports/dynamo.test_minifier_1.1_4b9c5f7fc1bc4da6_.log (stored 0%)
2025-03-04T21:11:05.7333784Z   adding: test/test-reports/dynamo.test_decorators_1.1_be30a4a939a6145b_.log (stored 0%)
2025-03-04T21:11:05.7334485Z   adding: test/test-reports/cpp.Dimname_test_1.1_2d085128394105a9_.log (deflated 59%)
2025-03-04T21:11:05.7335193Z   adding: test/test-reports/cpp.lazy_tensor_test_1.1_0fecc2aef53f444e_.log (deflated 54%)
2025-03-04T21:11:05.7335923Z   adding: test/test-reports/dynamo.test_aot_autograd_1.1_b4c05624317c19a1_.log (stored 0%)
2025-03-04T21:11:05.7336646Z   adding: test/test-reports/dynamo.test_functions_1.1_aeef6b18a1e558e7_.log (stored 0%)
2025-03-04T21:11:05.7337359Z   adding: test/test-reports/dynamo.test_backends_1.1_6b275b85ac783778_.log (stored 0%)
2025-03-04T21:11:05.7338161Z   adding: test/test-reports/cpp.cpu_generator_test_1.1_bd629cb135206cb7_.log (deflated 77%)
2025-03-04T21:11:05.7338918Z   adding: test/test-reports/dynamo.test_skip_non_tensor_1.1_8530e07b6b753139_.log (stored 0%)
2025-03-04T21:11:05.7339679Z   adding: test/test-reports/dynamo.test_python_autograd_1.1_7214439ed99fe7ac_.log (stored 0%)
2025-03-04T21:11:05.7340423Z   adding: test/test-reports/dynamo.test_pre_dispatch_1.1_e076652b44b4a8c8_.log (stored 0%)
2025-03-04T21:11:05.7341175Z   adding: test/test-reports/cpp.wrapdim_test_1.1_704c6acaceaa295c_.log (deflated 49%)
2025-03-04T21:11:05.7342010Z   adding: test/test-reports/cpp.dlconvertor_test_1.1_a37a6605d03e4926_.log (deflated 56%)
2025-03-04T21:11:05.7357475Z   adding: test/test-reports/dynamo.test_exceptions_1.1_382aa196860fe121_.log (stored 0%)
2025-03-04T21:11:05.7358418Z   adding: test/test-reports/dynamo.test_hooks_1.1_428f1b62efaa2c52_.log (stored 0%)
2025-03-04T21:11:05.7359229Z   adding: test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_e3f563a91fd10554_.log (stored 0%)
2025-03-04T21:11:05.7360014Z   adding: test/test-reports/cpp.basic_1.1_b0704dcb83617e92_.log (deflated 61%)
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2025-03-04T21:11:05.7366498Z   adding: test/test-reports/dynamo.test_deviceguard_1.1_025f3b8d9e062024_.log (stored 0%)
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2025-03-04T21:11:05.7369515Z   adding: test/test-reports/dynamo.test_bytecode_utils_1.1_ab3024ef557da339_.log (stored 0%)
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2025-03-04T21:11:05.7372492Z   adding: test/test-reports/dynamo.test_input_attr_tracking_1.1_633cd8f535a59955_.log (stored 0%)
2025-03-04T21:11:05.7373354Z   adding: test/test-reports/dynamo.test_aot_autograd_cache_1.1_cac7f4597b0c6a5d_.log (stored 0%)
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2025-03-04T21:11:05.7379708Z   adding: test/test-reports/optim.test_optim_1.1_856829bc0c37dcab_.log (deflated 7%)
2025-03-04T21:11:05.7454861Z   adding: test/test-reports/test_jit_fuser_te_1.1_a1e7d06cb9db8837_.log (deflated 96%)
2025-03-04T21:11:05.7455656Z   adding: test/test-reports/cpp.Dict_test_1.1_9fc6005578b9e0d7_.log (deflated 49%)
2025-03-04T21:11:05.7456426Z   adding: test/test-reports/cpp.Dimname_test_1.1_6e8a298ffb1be184_.log (deflated 49%)
2025-03-04T21:11:05.7457355Z   adding: test/test-reports/cpp.NamedTensor_test_1.1_8af96018782e7643_.log (deflated 49%)
2025-03-04T21:11:05.7458232Z   adding: test/test-reports/cpp.apply_utils_test_1.1_b65ae24d9acf615c_.log (deflated 49%)
2025-03-04T21:11:05.7458993Z   adding: test/test-reports/cpp.atest_1.1_1db38d63b5cf795b_.log (deflated 49%)
2025-03-04T21:11:05.7459722Z   adding: test/test-reports/cpp.basic_1.1_bd7c7cf7e9157da1_.log (deflated 49%)
2025-03-04T21:11:05.7460504Z   adding: test/test-reports/cpp.broadcast_test_1.1_d19d47621cf8a219_.log (deflated 49%)
2025-03-04T21:11:05.7461310Z   adding: test/test-reports/cpp.cpu_generator_test_1.1_c988927da08edac1_.log (deflated 49%)
2025-03-04T21:11:05.7462099Z   adding: test/test-reports/cpp.dlconvertor_test_1.1_e4b01fb443966257_.log (deflated 49%)
2025-03-04T21:11:05.7462858Z   adding: test/test-reports/cpp.extension_backend_test_1.1_c134e007ba8d9a20_.log (deflated 49%)
2025-03-04T21:11:05.7463605Z   adding: test/test-reports/cpp.lazy_tensor_test_1.1_1660bc018d21a8a4_.log (deflated 49%)
2025-03-04T21:11:05.7464324Z   adding: test/test-reports/cpp.legacy_vmap_test_1.1_db82642df822766d_.log (deflated 49%)
2025-03-04T21:11:05.7465028Z   adding: test/test-reports/cpp.native_test_1.1_cf1f93dcafb1ec50_.log (deflated 49%)
2025-03-04T21:11:05.7465732Z   adding: test/test-reports/cpp.operators_test_1.1_94c46a3c565350d9_.log (deflated 49%)
2025-03-04T21:11:05.7466990Z   adding: test/test-reports/cpp.scalar_tensor_test_1.1_59ec1100fee095ee_.log (deflated 49%)
2025-03-04T21:11:05.7468006Z   adding: test/test-reports/cpp.scalar_test_1.1_a353d035b4db374a_.log (deflated 49%)
2025-03-04T21:11:05.7468737Z   adding: test/test-reports/cpp.tensor_iterator_test_1.1_2f6801378270ebc5_.log (deflated 49%)
2025-03-04T21:11:05.7469505Z   adding: test/test-reports/cpp.undefined_tensor_test_1.1_ccdb19b2744d023b_.log (deflated 49%)
2025-03-04T21:11:05.7497524Z ##[group]Run # Remove any previous debugging artifacts if they exist
2025-03-04T21:11:05.7498187Z # Remove any previous debugging artifacts if they exist
2025-03-04T21:11:05.7498636Z rm -f debug-*.zip
2025-03-04T21:11:05.7498948Z if [ -d 'test/debug' ]; then
2025-03-04T21:11:05.7499335Z   zip -r "debug-${FILE_SUFFIX}.zip" test/debug
2025-03-04T21:11:05.7499772Z fi
2025-03-04T21:11:05.7505361Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:05.7505872Z env:
2025-03-04T21:11:05.7506124Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:05.7506632Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:05.7507293Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830
2025-03-04T21:11:05.7507835Z ##[endgroup]
2025-03-04T21:11:05.7590849Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T21:11:05.7591217Z with:
2025-03-04T21:11:05.7591466Z   s3-bucket: gha-artifacts
2025-03-04T21:11:05.7591820Z   s3-prefix: pytorch/pytorch/13661694839/1/artifact

2025-03-04T21:11:05.7592206Z   retention-days: 14
2025-03-04T21:11:05.7592493Z   if-no-files-found: warn
2025-03-04T21:11:05.7592791Z   path: test-jsons-*.zip
2025-03-04T21:11:05.7593077Z   name: artifact
2025-03-04T21:11:05.7593331Z   region: us-east-1
2025-03-04T21:11:05.7593570Z env:
2025-03-04T21:11:05.7593804Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:05.7594299Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:05.7594840Z ##[endgroup]
2025-03-04T21:11:06.1187966Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-04T21:11:06.1188780Z With the provided path, there will be 1 file uploaded
2025-03-04T21:11:06.1189293Z Uploading to s3 prefix: pytorch/pytorch/13661694839/1/artifact
2025-03-04T21:11:06.1266739Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:06.3160789Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:06.3344381Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T21:11:06.3344850Z with:
2025-03-04T21:11:06.3345112Z   s3-bucket: gha-artifacts
2025-03-04T21:11:06.3345475Z   s3-prefix: pytorch/pytorch/13661694839/1/artifact

2025-03-04T21:11:06.3345871Z   retention-days: 14
2025-03-04T21:11:06.3346161Z   if-no-files-found: error
2025-03-04T21:11:06.3346475Z   path: test-reports-*.zip
2025-03-04T21:11:06.3346775Z   name: artifact
2025-03-04T21:11:06.3347035Z   region: us-east-1
2025-03-04T21:11:06.3347296Z env:
2025-03-04T21:11:06.3347542Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:06.3348041Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:06.3348581Z ##[endgroup]
2025-03-04T21:11:06.6589607Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-04T21:11:06.6590113Z With the provided path, there will be 1 file uploaded
2025-03-04T21:11:06.6590601Z Uploading to s3 prefix: pytorch/pytorch/13661694839/1/artifact
2025-03-04T21:11:06.6628453Z Starting upload of test-reports-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:06.9244129Z Finished upload of test-reports-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:06.9431136Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T21:11:06.9431508Z with:
2025-03-04T21:11:06.9431757Z   s3-bucket: gha-artifacts
2025-03-04T21:11:06.9432122Z   s3-prefix: pytorch/pytorch/13661694839/1/artifact

2025-03-04T21:11:06.9432501Z   retention-days: 14
2025-03-04T21:11:06.9432782Z   if-no-files-found: ignore
2025-03-04T21:11:06.9433084Z   path: logs-*.zip
2025-03-04T21:11:06.9433347Z   name: artifact
2025-03-04T21:11:06.9433593Z   region: us-east-1
2025-03-04T21:11:06.9433847Z env:
2025-03-04T21:11:06.9434098Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:06.9434597Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:06.9435130Z ##[endgroup]
2025-03-04T21:11:07.2704307Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-04T21:11:07.2704850Z With the provided path, there will be 1 file uploaded
2025-03-04T21:11:07.2705330Z Uploading to s3 prefix: pytorch/pytorch/13661694839/1/artifact
2025-03-04T21:11:07.2743423Z Starting upload of logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:07.5007078Z Finished upload of logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:07.5191656Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T21:11:07.5192030Z with:
2025-03-04T21:11:07.5192281Z   s3-bucket: gha-artifacts
2025-03-04T21:11:07.5192637Z   s3-prefix: pytorch/pytorch/13661694839/1/artifact

2025-03-04T21:11:07.5193023Z   retention-days: 14
2025-03-04T21:11:07.5193308Z   if-no-files-found: ignore
2025-03-04T21:11:07.5193755Z   path: debug-*.zip
2025-03-04T21:11:07.5194026Z   name: artifact
2025-03-04T21:11:07.5194276Z   region: us-east-1
2025-03-04T21:11:07.5194532Z env:
2025-03-04T21:11:07.5194774Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:07.5195278Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:07.5195817Z ##[endgroup]
2025-03-04T21:11:07.8418311Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded.
2025-03-04T21:11:07.8610177Z ##[group]Run # shellcheck disable=SC2156
2025-03-04T21:11:07.8610589Z # shellcheck disable=SC2156
2025-03-04T21:11:07.8611227Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2025-03-04T21:11:07.8617072Z shell: /usr/bin/bash -e {0}
2025-03-04T21:11:07.8617377Z env:
2025-03-04T21:11:07.8617623Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:07.8618243Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:07.8618790Z ##[endgroup]
2025-03-04T21:11:08.0961783Z Prepare all required actions
2025-03-04T21:11:08.0962225Z Getting action download info
2025-03-04T21:11:08.2504375Z ##[group]Run ./.github/actions/upload-utilization-stats
2025-03-04T21:11:08.2504885Z with:
2025-03-04T21:11:08.2505130Z   job_id: 38194769830
2025-03-04T21:11:08.2505572Z   job_name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T21:11:08.2506094Z   workflow_name: pull
2025-03-04T21:11:08.2506381Z   workflow_run_id: 13661694839
2025-03-04T21:11:08.2506687Z   workflow_attempt: 1
2025-03-04T21:11:08.2506956Z env:
2025-03-04T21:11:08.2507194Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:08.2507693Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:08.2508223Z ##[endgroup]
2025-03-04T21:11:08.2531040Z ##[group]Run echo "workflow_id: 13661694839"
2025-03-04T21:11:08.2531453Z echo "workflow_id: 13661694839"
2025-03-04T21:11:08.2531856Z echo "workflow_attempt: 1"
2025-03-04T21:11:08.2532203Z echo "workflow_Name: pull"
2025-03-04T21:11:08.2532539Z echo "job_id: 38194769830"
2025-03-04T21:11:08.2533098Z echo "job_name:  linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)"
2025-03-04T21:11:08.2539171Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:08.2539585Z env:
2025-03-04T21:11:08.2539836Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:08.2540338Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:08.2540880Z ##[endgroup]
2025-03-04T21:11:08.2563745Z workflow_id: 13661694839
2025-03-04T21:11:08.2564067Z workflow_attempt: 1
2025-03-04T21:11:08.2564344Z workflow_Name: pull
2025-03-04T21:11:08.2564604Z job_id: 38194769830
2025-03-04T21:11:08.2565211Z job_name:  linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)
2025-03-04T21:11:08.2605275Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-04T21:11:08.2605629Z with:
2025-03-04T21:11:08.2605863Z   shell: bash
2025-03-04T21:11:08.2606111Z   timeout_minutes: 5
2025-03-04T21:11:08.2606380Z   max_attempts: 5
2025-03-04T21:11:08.2606646Z   retry_wait_seconds: 30
2025-03-04T21:11:08.2607154Z   command: set -eu
python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3

2025-03-04T21:11:08.2607706Z   polling_interval_seconds: 1
2025-03-04T21:11:08.2608019Z   warning_on_retry: true
2025-03-04T21:11:08.2608313Z   continue_on_error: false
2025-03-04T21:11:08.2608598Z env:
2025-03-04T21:11:08.2608835Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:08.2609420Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:08.2609937Z ##[endgroup]
2025-03-04T21:11:08.5883884Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T21:11:08.6697433Z Collecting python-dateutil==2.8.2
2025-03-04T21:11:08.6923838Z   Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
2025-03-04T21:11:09.6475516Z Collecting boto3==1.35.42
2025-03-04T21:11:09.6629038Z   Downloading boto3-1.35.42-py3-none-any.whl (139 kB)
2025-03-04T21:11:10.1763693Z Collecting pandas==2.1.3
2025-03-04T21:11:10.1801099Z   Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)
2025-03-04T21:11:10.3186140Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0)
2025-03-04T21:11:10.3233396Z 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)
2025-03-04T21:11:10.3237431Z 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)
2025-03-04T21:11:10.3242730Z 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)
2025-03-04T21:11:10.3876489Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1)
2025-03-04T21:11:10.4189887Z Collecting tzdata>=2022.1
2025-03-04T21:11:10.4227658Z   Downloading tzdata-2025.1-py2.py3-none-any.whl (346 kB)
2025-03-04T21:11:11.2441457Z Collecting numpy<2,>=1.22.4
2025-03-04T21:11:11.2480562Z   Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
2025-03-04T21:11:11.4483684Z 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)
2025-03-04T21:11:11.6204935Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3
2025-03-04T21:11:16.5835464Z   Attempting uninstall: boto3
2025-03-04T21:11:16.5836010Z     Found existing installation: boto3 1.35.33
2025-03-04T21:11:16.5930864Z     Uninstalling boto3-1.35.33:
2025-03-04T21:11:16.5943178Z       Successfully uninstalled boto3-1.35.33
2025-03-04T21:11:16.6478033Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.1
2025-03-04T21:11:17.3425394Z Command completed after 1 attempt(s).
2025-03-04T21:11:17.3478009Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-04T21:11:17.3478768Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-04T21:11:17.3479409Z   --workflow-run-id "13661694839" \
2025-03-04T21:11:17.3479793Z   --workflow-name "pull" \
2025-03-04T21:11:17.3480154Z   --workflow-run-attempt "1" \
2025-03-04T21:11:17.3480505Z   --job-id "38194769830" \
2025-03-04T21:11:17.3481086Z   --job-name "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)"
2025-03-04T21:11:17.3486866Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:17.3487278Z env:
2025-03-04T21:11:17.3487597Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:17.3488115Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:17.3488660Z ##[endgroup]
2025-03-04T21:11:18.9380831Z repo: pytorch/pytorch
2025-03-04T21:11:18.9381344Z Downloading logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_38194769830.zip
2025-03-04T21:11:18.9381907Z Converted Log Model: UtilizationMetadata:
2025-03-04T21:11:18.9383314Z  UtilizationMetadata(level='metadata', workflow_id='13661694839', job_id='38194769830', workflow_name='pull', job_name='linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741118733, gpu_count=0, cpu_count=8, gpu_type='', error=None)
2025-03-04T21:11:18.9385045Z [Db Segments] detected pytest cmd: 12, generated segments: 12
2025-03-04T21:11:18.9385484Z [db model] Peek db timeseries 
2025-03-04T21:11:18.9385787Z :{
2025-03-04T21:11:18.9386018Z     "created_at": 1741122678,
2025-03-04T21:11:18.9386327Z     "type": "utilization",
2025-03-04T21:11:18.9386608Z     "tags": [
2025-03-04T21:11:18.9386846Z         "record"
2025-03-04T21:11:18.9387104Z     ],
2025-03-04T21:11:18.9387343Z     "time_stamp": 1741118733,
2025-03-04T21:11:18.9387654Z     "repo": "pytorch/pytorch",
2025-03-04T21:11:18.9387951Z     "workflow_id": 13661694839,
2025-03-04T21:11:18.9388256Z     "run_attempt": 1,
2025-03-04T21:11:18.9388530Z     "job_id": 38194769830,
2025-03-04T21:11:18.9388824Z     "workflow_name": "pull",
2025-03-04T21:11:18.9389325Z     "job_name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)",
2025-03-04T21:11:18.9389844Z     "json_data": "{}"
2025-03-04T21:11:18.9390103Z }
2025-03-04T21:11:18.9390639Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661694839/1/38194769830/metadata
2025-03-04T21:11:18.9391612Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661694839/1/38194769830/metadata 
2025-03-04T21:11:18.9392618Z Writing 782 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661694839/1/38194769830/time_series
2025-03-04T21:11:18.9393818Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661694839/1/38194769830/time_series 
2025-03-04T21:11:19.0531751Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2025-03-04T21:11:19.0532297Z with:
2025-03-04T21:11:19.0532529Z env:
2025-03-04T21:11:19.0532768Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:19.0533272Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:19.0533803Z ##[endgroup]
2025-03-04T21:11:19.0555622Z ##[group]Run set -eou pipefail
2025-03-04T21:11:19.0555990Z set -eou pipefail
2025-03-04T21:11:19.0556291Z 
2025-03-04T21:11:19.0556692Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2025-03-04T21:11:19.0557193Z for _ in $(seq 1440); do
2025-03-04T21:11:19.0557556Z     # Break if no ssh session exists anymore
2025-03-04T21:11:19.0557937Z     if [ "$(who)" = "" ]; then
2025-03-04T21:11:19.0558268Z       break
2025-03-04T21:11:19.0558532Z     fi
2025-03-04T21:11:19.0558829Z     echo "."
2025-03-04T21:11:19.0559105Z     sleep 5
2025-03-04T21:11:19.0559381Z done
2025-03-04T21:11:19.0564989Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:19.0565400Z env:
2025-03-04T21:11:19.0565640Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:19.0566141Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:19.0566682Z ##[endgroup]
2025-03-04T21:11:19.0589437Z Holding runner for 2 hours until all ssh sessions have logged out
2025-03-04T21:11:19.0666890Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T21:11:19.0667495Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T21:11:19.0667958Z # shellcheck disable=SC2046
2025-03-04T21:11:19.0668325Z docker stop $(docker ps -q) || true
2025-03-04T21:11:19.0668703Z # Prune all of the docker images
2025-03-04T21:11:19.0669060Z docker system prune -af
2025-03-04T21:11:19.0675806Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:19.0676233Z env:
2025-03-04T21:11:19.0676484Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:19.0676984Z   DOCKER_CONTAINER_ID: 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:19.0677525Z ##[endgroup]
2025-03-04T21:11:20.0969697Z 4b477976dd7b
2025-03-04T21:11:20.6301245Z Deleted Containers:
2025-03-04T21:11:20.6301923Z 4b477976dd7b4d67afe62e4d7e479b8a3686dcfe0a7806f283d847d6f5efdd57
2025-03-04T21:11:20.6302291Z 
2025-03-04T21:11:27.7716279Z Deleted Images:
2025-03-04T21:11:27.7717306Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:11:27.7719067Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10@sha256:4b06140a996af551b7b0ac83f477dc941e0c9222ab7629a7b7ab8abac73604c2
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2025-03-04T21:11:27.7797817Z 
2025-03-04T21:11:27.7797972Z Total reclaimed space: 10.71GB
2025-03-04T21:11:27.7910423Z Post job cleanup.
2025-03-04T21:11:27.7984232Z Post job cleanup.
2025-03-04T21:11:27.9010046Z [command]/usr/bin/git version
2025-03-04T21:11:27.9061772Z git version 2.47.1
2025-03-04T21:11:27.9097974Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/677348f5-38d2-45f6-a259-99e2c7bf43c3/.gitconfig'
2025-03-04T21:11:27.9107150Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/677348f5-38d2-45f6-a259-99e2c7bf43c3' before making global git config changes
2025-03-04T21:11:27.9108175Z Adding repository directory to the temporary git global config as a safe directory
2025-03-04T21:11:27.9120571Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T21:11:27.9156611Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-04T21:11:27.9185964Z [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' || :"
2025-03-04T21:11:27.9495256Z Entering 'android/libs/fbjni'
2025-03-04T21:11:27.9547113Z Entering 'third_party/FP16'
2025-03-04T21:11:27.9598385Z Entering 'third_party/FXdiv'
2025-03-04T21:11:27.9648791Z Entering 'third_party/NNPACK'
2025-03-04T21:11:27.9698516Z Entering 'third_party/NVTX'
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2025-03-04T21:11:28.2396399Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
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2025-03-04T21:11:28.2495153Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
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2025-03-04T21:11:28.3019526Z Entering 'third_party/sleef'
2025-03-04T21:11:28.3068994Z Entering 'third_party/tensorpipe'
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2025-03-04T21:11:28.3167375Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-04T21:11:28.3220526Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-04T21:11:28.3269215Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T21:11:28.3317103Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T21:11:28.3384852Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-04T21:11:28.3403979Z http.https://github.com/.extraheader
2025-03-04T21:11:28.3414017Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2025-03-04T21:11:28.3443263Z [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' || :"
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2025-03-04T21:11:28.5764597Z Entering 'third_party/kineto'
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2025-03-04T21:11:28.5828840Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
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2025-03-04T21:11:28.5958271Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
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2025-03-04T21:11:28.6087554Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
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2025-03-04T21:11:28.6149726Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
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2025-03-04T21:11:28.6215261Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
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2025-03-04T21:11:28.6279335Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
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2025-03-04T21:11:28.6342481Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
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2025-03-04T21:11:28.6472953Z Entering 'third_party/kineto/libkineto/third_party/fmt'
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2025-03-04T21:11:28.6536051Z Entering 'third_party/kineto/libkineto/third_party/googletest'
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2025-03-04T21:11:28.6602605Z Entering 'third_party/kleidiai'
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2025-03-04T21:11:28.6667096Z Entering 'third_party/mimalloc'
2025-03-04T21:11:28.6700683Z http.https://github.com/.extraheader
2025-03-04T21:11:28.6731621Z Entering 'third_party/nlohmann'
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2025-03-04T21:11:28.6879888Z Entering 'third_party/onnx/third_party/pybind11'
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2025-03-04T21:11:28.6945752Z Entering 'third_party/opentelemetry-cpp'
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2025-03-04T21:11:28.7013071Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
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2025-03-04T21:11:28.7076193Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-04T21:11:28.7108830Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7139206Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
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2025-03-04T21:11:28.7202150Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-04T21:11:28.7235047Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7266574Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-04T21:11:28.7299233Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7330083Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-04T21:11:28.7363812Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7394077Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T21:11:28.7427013Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7457469Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
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2025-03-04T21:11:28.7524093Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-04T21:11:28.7556554Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7590498Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T21:11:28.7623322Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7674323Z Entering 'third_party/pocketfft'
2025-03-04T21:11:28.7711017Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7742199Z Entering 'third_party/protobuf'
2025-03-04T21:11:28.7777020Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7812415Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-04T21:11:28.7845919Z http.https://github.com/.extraheader
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2025-03-04T21:11:28.7908686Z http.https://github.com/.extraheader
2025-03-04T21:11:28.7940681Z Entering 'third_party/psimd'
2025-03-04T21:11:28.7975889Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8007240Z Entering 'third_party/pthreadpool'
2025-03-04T21:11:28.8040741Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8070982Z Entering 'third_party/pybind11'
2025-03-04T21:11:28.8104393Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8135921Z Entering 'third_party/python-peachpy'
2025-03-04T21:11:28.8170241Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8202941Z Entering 'third_party/sleef'
2025-03-04T21:11:28.8237545Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8268880Z Entering 'third_party/tensorpipe'
2025-03-04T21:11:28.8302951Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8334177Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-04T21:11:28.8368097Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8398960Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-04T21:11:28.8432062Z http.https://github.com/.extraheader
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2025-03-04T21:11:28.8495281Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8525359Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T21:11:28.8557827Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8587589Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T21:11:28.8620597Z http.https://github.com/.extraheader
2025-03-04T21:11:28.8736755Z A job completed hook has been configured by the self-hosted runner administrator
2025-03-04T21:11:28.8762888Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2025-03-04T21:11:28.8767973Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:28.8768397Z ##[endgroup]
2025-03-04T21:11:36.5457245Z Cleaning up orphan processes